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📼 Replay de la session "case studies" de présentation de 3 projets Innovation Lab 2024-2025 sélectionnés du 10 février 2026

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📼 Replay de la session "case studies" de présentation de 3 projets Innovation Lab 2024-2025 sélectionnés du 10 février 2026

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Creative Innovation Lab 2026 call: Show cases with 2024-2025 beneficiaries projects - 10/02/2026

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Replay du webinaire #3 de préparation du jeudi 12 février 2026

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Replay du webinaire #3 de préparation du jeudi 12 février 2026

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Europe Créative transsectoriel appel Innovation Lab 2026 webinaire #3 de préparation 12/02/2026

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Support du webinaire #3 de préparation du jeudi 12 février 2026

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Support du webinaire #3 de préparation du jeudi 12 février 2026

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20260212 Atelier préparation #3 Innovation Lab 2026

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Replay webinaire dédié aux lauréats France 2030 - en partenariat avec le SGPI et la DGMIC du Ministère de la Culture du 6 février 2026

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Replay webinaire dédié aux lauréats France 2030 - en partenariat avec le SGPI et la DGMIC du Ministère de la Culture du 6 février 2026

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Webinaire appel Europe Créative transsectoriel Innovation Lab 2026 pour les lauréats France 2030

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Replay #2 de préparation de l'appel Innovation Lab 2026 du 23 janvier 2026

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Replay #2 de préparation de l'appel Innovation Lab 2026 du 23 janvier 2026

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Europe Créative transsectoriel appel Innovation Lab 2026 webinaire #2 de préparation du 23/01/2026

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📼 Replay de la session "case studies" de présentation de 4 projets Innovation Lab 2023-2024 sélectionnés du 6 février 2025

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📼 Replay de la session "case studies" de présentation de 4 projets Innovation Lab 2023-2024 sélectionnés du 6 février 2025

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#1 Visionnary Tales (2024) : WEDAVINCI GmbH (DE) / Veronika Gamper

#2 GameBook PIC Platform (2024) : Gamebook Studio HQ GmbH (DE) / Ulrike Küchler

#3 Emerge (2023) : Stichting Eurosonic Noorderslag (NL) / Oskar Strajn

#4 Auvent (2024) : TheGreenShot (BE) / Véronique Pevtschin

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Creative Innovation Lab: Learn from selected projects 2023-2024 and Matching data base - 06/02/2025

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#1 Présentation du projet Visionnary Tales (2024) : WEDAVINCI GmbH (DE)

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#1 Présentation du projet Visionnary Tales (2024) : WEDAVINCI GmbH (DE)

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Présentation du projet GamperGameBook PIC Platform (2024) : Gamebook Studio HQ GmbH (DE)

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Présentation du projet GamperGameBook PIC Platform (2024) : Gamebook Studio HQ GmbH (DE)

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Présentation du projet UlrikeKüchlerEmerge (2023) : Stichting Eurosonic Noorderslag (NL)

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Présentation du projet UlrikeKüchlerEmerge (2023) : Stichting Eurosonic Noorderslag (NL)

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Présentation du projet StrajnAuvent (2024) : TheGreenShot (BE)

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Présentation du projet StrajnAuvent (2024) : TheGreenShot (BE)

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Support du webinaire #2 de préparation à l'appel Innovation Lab 2026 du 23 janvier 2026

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Support du webinaire #2 de préparation à l'appel Innovation Lab 2026 du 23 janvier 2026

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20260122 Atelier préparation #2 Innovation Lab 2026-2

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📼 Replay de la session "case studies" de présentation de 5 projets Innovation Lab 2023 sélectionnés

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📼 Replay de la session "case studies" de présentation de 5 projets Innovation Lab 2023 sélectionnés

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Cette session a été proposé le 2 février 2024, en partenariat avec la Commission européenne, l'EACEA et les Bureaux Europe Créative

#1 CREA Trust AI / PanOdyssey (FR) - Alexandre Leforestier #2 EBB-AI / EBB (FR) - Anthony Lejuez

#3 CrossCult Young Pros / PAZZ GmbH (DE) - Olfa Kaestner #4 Green Producers Tool / Babusjka (NO) - Mads Astrup Rønning

#5 CRAVE AI / CRAVE (PL) - Michalina Biernacka & Tomasz Polit

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Creative Innovation Lab: Learn from selected projects 2023! - Case studies & Matching Tool

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Enter EU Login information of the future LEAR. If the LEAR does not have an EU Login yet, the email address and the names entered will be used to create a new account.

If the email address of the future LEAR has not been registered yet, you have the option to invite the person to register:

By clicking Continue, an invitation to register is sent to the provided email address. You must wait for the LEAR to accept the invitation before you can continue providing LEAR data.

If the entered email address exists in the EU Login database but either the first or the last name being provided do not match what has been registered in the database, you need to correct the details. It is not possible to use a registered email address with mismatching naming details:

In this case, you have 2 options: ask the future LEAR to modify first name and/or last name accordingly directly in their EU Login account, or enter a different email address.

If no problems are found, you may click on Next to continue.

In the following step, the LEAR data screen displays, fill it in accordingly:

Click Copy the organisation's contact details if you want to reuse details of the organisation previously provided in the registration process.

Make sure to enter a mobile phone number for the LEAR, so that the activation PIN for the LEAR account is sent by SMS.

When the data has been provided in the LEAR data screen, click to move to the following screen. Use instead to exit the LEAR wizard saving your changes. Save

Enter the first and last names, and the position of the person who can represent your organisation in the Legal representative data step. They will have to sign the original documents required for appointing a LEAR. This can be the CEO of the company, the rector of the university, the Director-General of the institute, etc.

In this step, you are prompted to submit electronically both the LEAR Appointment Documents, and the Legal Representative and LEAR proof of identities:

When applicable, you are provided with a pre-filled PDF document where the data you entered in the LEAR registration wizard has been used. Click on the PDF icon to download it, then print it, get it signed and upload it again using the arrow button. A red arrow means that the document is still to be uploaded. When the document has been provided, the arrow turns yellow.

Also, when a document has been provided, the time and date of the upload appears, together with the name of the file, next to its status. Click on it to display the content, except for the identity documents, which cannot be previewed due to privacy reasons.

Note

The Submit button is not available until all documents have been provided. If you do changes to the previously saved LEAR data and Legal representative data, the changes will be listed when coming back to the documents screen and the previously uploaded documents, except the ID related files, will be removed. This is so because new templates will have been generated at this point, containing the updated data, and you need to sign and upload new ones.

Important

LEAR replacement

The process to change the current LEAR is the same as the one to appoint the LEAR. However, you need to meet the following condition for you to replace the LEAR in the Participant register:

You are the current (validated) LEAR of the organisation, or you have been appointed as Account administrator for that specific PIC. Otherwise, you are not be able to edit the data.

To replace the LEAR, click on the Replace LEAR button in the LEAR section. You can see that the status of the current LEAR is Validated. After editing the LEAR details and sending for validation, the status will change into Data provided again:

Follow the same steps as you do when you appoint the LEAR for the first time. The appointed LEAR needs to have an existing EU Login account linked to their email address, otherwise this person will need to create their account first, and you will be able to resume the replacement afterwards.

Important

Nevertheless:

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Enter EU Login information of the future LEAR. If the LEAR does not have an EU Login yet, the email address and the names entered will be used to create a new account.

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The Funding and Tenders Portal is the single entry point (the Single Electronic Data Interchange Area) for applicants, contractors and experts in funding programmes and procurements managed by the European Commission.

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🗓 Deadline de l'appel : 23 avril 2026 à 17h (heure de Paris)

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🗓 Deadline de l'appel : 23 avril 2026 à 17h (heure de Paris)

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Page officielle de l'appel Innovation Lab 2026

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Page officielle de l'appel Innovation Lab 2026

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The Funding and Tenders Portal is the single entry point (the Single Electronic Data Interchange Area) for applicants, contractors and experts in funding programmes and procurements managed by the European Commission.

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H2020 Online Manual Grants From evaluation to grant signature Enter & submit grant data Ethics review Security scrutiny

Preparing grants

Key points

Invitation to prepare a grant agreement: After assessment, if your proposal has been proposed for funding, we will send you the 'Evaluation Summary Report' and ask you to help prepare a grant agreement.

Online grant preparation: Please use the Funding & Tenders Portal to provide any legal and administrative details not included in the proposal.

Follow your proposal: Horizon 2020 actions (i.e. projects) must be implemented in accordance with the assessed proposal. The grant agreement must not differ from the proposal. This is without prejudice to corrections needed

as a result of an ethical review or security scrutiny

to ensure the project conforms to the applicable rules, e.g. legal and financial rules

to remove clerical errors or clear inconsistencies

when, under exceptional circumstances, a participant is removed from a consortium during grant preparation

Shortcomings: You may correct shortcomings identified by the experts in the 'Evaluation Summary Report', provided that this does not delay your part in preparing the grant agreement beyond the applicable deadlines.

Electronic signatures: You do not need to sign a hard copy of the grant agreement, accession form, or 'Declaration of Honour'. All signatures are electronic.

Timeframe: The agreement must be signed at the latest 3 months after starting the grant preparation.

Consortium agreement: The members of the consortium are also obliged to sign a consortium agreement between each other. For more information see Annotated Model Grant Agreement, Article 41.3 and the guidance on establishing a consortium agreement.

Aim of the grant preparation

gather legal, administrative and financial information from you as a prospective beneficiary (project participant) and any third parties linked to you

make sure the Description of the Action (DoA, Annex 1 to the grant agreement) and the Estimated budget for the action/Estimated Lump Sum Breakdown for lump sum pilot projects (Annex 2) match the proposal, any relevant information in the invitation to prepare the grant agreement, and, if applicable, the ethics review report, and the security scrutiny report

establish the key points of the grant agreement (when the project starts, reporting periods, amount of pre-financing, need for consortium agreement, and, if applicable, ethical issues, third parties linked to the beneficiaries, in-kind contributions provided by third parties, subcontracting)

verify your financial capacity (principally, this concerns certain types of coordinators only, other participants will be check in exceptional cases; for details see next page)

Please use the Funding & Tenders Portal's My Projects page to prepare your grant by entering the necessary data and sign your grant agreement online. These data will also be available in PDF format.

Grant preparation process

This process summarises the main steps to finalise the signature of your grant agreement:

1. Enter & submit grant data

Entering legal, administrative & financial data

Pre-financing

Preparing the Description of the Action (DoA), Annex 1

2. Ethics review (ethics screening & assessment)

3. Security scrutiny

4. Signing the grant agreement

Notifications

You will receive e-mail notifications about all the actions that you need to carry out for the grant preparation.
For more detail, please see the list of all the grant-related notifications and their recipients.

Termination of the grant preparation

If the consortium decided not to sign their grant during the grant preparation phase, they can launch the termination process through the grant management IT tool. See the detailed screens and descriptions in the on termination before signing the grant.

You can also find more information on the Beneficiary Termination Reporting process or how to launch a Grant Agreement Termination request.

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europa european commission horizon 2020 h2020 online manual funding guide guidance

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H2020 Online Manual Grants From evaluation to grant signature Enter & submit grant data Ethics review Security scrutiny Key points Invitation to prepare a grant agreement: After assessment, if your proposal has been proposed for funding, we will send you the 'Evaluation Summary Report' and ask you to help prepare a grant agreement.

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🗓 Prochain RDV en ligne: Q&A#1 le vendredi 10 avril (10h-12h) et Q&A#2 le mercredi 15 avril (10h-12h) (heure de Paris)

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🗓 Prochain RDV en ligne: Q&A#1 le vendredi 10 avril (10h-12h) et Q&A#2 le mercredi 15 avril (10h-12h) (heure de Paris)

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Pour accompagner les candidats déposants

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Appel transsectoriel Creative Innovation Lab 2026

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Un lien d’accès pour le webinaire vous sera envoyé le jour précédent la réunion.

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Initially, only the person who first registered the organisation or any other appointed Self-registrant

once appointed, only the appointed representative (LEAR) and appointed Account Administrators.

To change the information on your organisation, go to My Organisations > Actions > Modify Organisation .

Please upload legal/financial supporting documents for any updates you submit so that the Central Validation Service can validate them. Maximum size for uploads: 6MB per file.

If your organisation changes status or address, your LEAR should immediately update the information in the Participant Register ( My Organisations > Actions > Modify Organisation ) and, if possible, directly inform (via your Coordinator) your EU Project Officers in ongoing grants (Coordinator uses the Portal Messaging Facility: My Projects > Actions > Manage Project > Process communications > New message ).

Some changes like, for instance, partial or universal transfers of rights will moreover require amendments in case you have ongoing Grant Agreements/Procurement Contracts.

If organisation roles need to be changed, your LEAR should immediately update them in the system ( My Organisations > Actions > Edit Organisation Roles ).

If project/contract roles need to be changed, your CoCo/PaCo should immediately update them in the system ( My Projects > Actions > Project Consortium ).

If grants/contracts or cost claims/invoices are signed in the system by persons whose access rights should have been revoked, this will be your responsibility and you will have to bear the consequences. Their signatures and other actions will be considered valid by the EU services.

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Data updates - Online Manual - Funding Tenders Opportunities

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To change the information on your organisation, go to My Organisations > Actions > Modify Organisation.

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L’agence exécutive européenne pour l'éducation et la culture a mis en ligne un guide sur l'utilisation du portail Funding & Tenders

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Participant Register - Register your organisation

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If you want to submit a proposal, your organisation needs to be registered in the Portal Participant Register and have a 9-digit Participant Identification Code (PIC). If your proposal is successful, you will receive a notification and be asked to:

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132 rue du Faubourg Saint-Denis
75010 Paris
Tel : 01 53 40 95 10

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Page de présentation de l'appel transsectoriel Innovation Lab sur le site du Relais Culture Europe

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Procédure de dépôt

Les nouveaux candidats doivent d'abord obtenir un code d'identification du participant (PIC) en s'enregistrant pour un EU Login et en saisissant les informations relatives à leur entreprise dans le portail des Funding and Tenders Portal de la Commission européenne.

Les nouveaux candidats doivent d'abord obtenir un code d'identification du participant (PIC) en s'enregistrant avec leur EU Login et en saisissant les informations relatives à leur entreprise dans le portail des Funding and Tenders Portal de la Commission européenne.
Structure du dossier
Votre demande doit être lisible, accessible et imprimable.

Votre dossier de candidature est divisé en 4 parties :

1. La partie A comprend des informations administratives sur les organisations candidates et votre budget prévisionnel résumé. Remplissez-la directement en ligne (voir edit forms).

2. La partie B décrit votre projet européen. Ce document est très important : il constitue l'objet de votre candidature qui sera évaluée par des experts indépendants.

Téléchargez le modèle à partir du système de soumission. Nous conseillons fortement de convertir ce fichier .rft en format Word pour une meilleure lisibilité. Une fois finalisé, téléchargez-le sous format de fichier PDF.

La partie B est limitée à 70 pages maximum. Les évaluateurs ne tiendront pas compte des pages supplémentaires.

Ce document est commun à l'ensemble des appels du programme Europe créative. Vous devez impérativement répondre à l'énoncé spécifique à votre appel pour chaque question. Si la mention N/A est présente, alors vous ne devez pas répondre.

Pour cet appel, les activités de votre projet doivent être présentées selon les work packages suivants :

WP 1 : Management de projet (obligatoire)

Des work packages additionnels peuvent être rajoutés.

La partie C contient des données additionnelles concernant votre projet, à remplir directement en ligne sur la plateforme Funding and Tenders

pièces annexes à joindre à la candidature

La partie B - descriptif de votre projet ("Application form Part B")

La document intitulé "Information on Independence and Ownership and control"

Pour ces pièces annexes, les formulaires obligatoires sont à télécharger sur la plateforme Funding & Tenders, dans l'espace dédié de l'appel, après vous être identifié.e et cliqué sur "START SUBMISSION".

points d'attention

L'ensemble des documents doivent être fourni dans le dossier de candidature, les liens vers des fichiers téléchargeables sont interdits (type dropbox,...) hormis pour des supports audiovisuels (vidéo,...).

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Les nouveaux candidats doivent d'abord obtenir un code d'identification du participant (PIC) en s'enregistrant pour un EU Login et en saisissant les informations relatives à leur entreprise dans le portail des Funding and Tenders Portal de la Commission européenne.

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How to find and apply for funding opportunities

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How to find and apply for funding opportunities

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European Education and Culture Executive Agency

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Notre mission est de soutenir les pratiques d'innovation culturelle européenne et le développement de projets, de réseaux de coopération et de communautés d'échanges et de recherche européennes entre acteur.rice.s engagé.e.s ou souhaitant s'engager sur les questions culturelles européennes. Nous proposons à chacun des outils pour s'informer, s'orienter, se renforcer et développer de nouveaux projets.

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Rapport de la Commission européenne - Opportunities & Challenges of Artificial Intelligence for the Cultural and Creative Sectors

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Rapport de la Commission européenne - Opportunities & Challenges of Artificial Intelligence for the Cultural and Creative Sectors

# data[].attributes.headline
Rapport de la Commission européenne - Study on copyright and new technologies

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Rapport de la Commission européenne - Study on copyright and new technologies

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EC report - Study on copyright and new technologies.en

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FAQ de l'appel Innovation 2026 - mise à jour régulièrement

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FAQ de l'appel Innovation 2026 - mise à jour régulièrement

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Résumé des projets sélectionnés sur l'appel Innovation Lab 2024 (FR)

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Résumé des projets sélectionnés sur l'appel Innovation Lab 2024 (FR)

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Résumé des projets - Innovation Lab 2024 (FR)

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Next Generation Virtual Worlds: Societal, Technological, Economicand Policy Challenges for the EU (JRC report - 2023)

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Next Generation Virtual Worlds: Societal, Technological, Economicand Policy Challenges for the EU (JRC report - 2023)

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Résumé des projets sélectionnés sur INNOVATION LAB 2023

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Résumé des projets sélectionnés sur INNOVATION LAB 2023

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Résumé des projets Innovation Lab 2022 sélectionnés

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Résumé des projets Innovation Lab 2022 sélectionnés

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Résumé des projets sélectionnés Innovation Lab 2021

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Résumé des projets sélectionnés Innovation Lab 2021

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Les résumés en anglais de ces projets sont disponibles sur la plateforme Funding & Tenders sur la page de l'appel Innovation Lab 2021

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TRANSSECCTORIEL - Innovation Lab 2021 - résumé des projets sélectionnés

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European Media Industry Report (mai 2023)

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European Media Industry Report (mai 2023)

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Ce rapport présente des tendances pour les secteurs audiovisuels, des jeux vidéos et des médias d'information - et notamment le potentiel offert par les mondes virtuels pour ces secteurs.

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Une initiative de l’UE sur le web 4.0 et les mondes virtuels: prendre de l’avance pour la prochaine transition technologique (CE, juillet 2023) PARTIE 1

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Une initiative de l’UE sur le web 4.0 et les mondes virtuels: prendre de l’avance pour la prochaine transition technologique (CE, juillet 2023) PARTIE 1

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The VR/AR Industrial Coalition - strategic paper (Mai 2022)

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The VR/AR Industrial Coalition - strategic paper (Mai 2022)

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Une initiative de l’UE sur le web 4.0 et les mondes virtuels: prendre de l’avance pour la prochaine transition technologique (CE, juillet 2023) PARTIE 2

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Une initiative de l’UE sur le web 4.0 et les mondes virtuels: prendre de l’avance pour la prochaine transition technologique (CE, juillet 2023) PARTIE 2

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Staff Working Document Citizens’ Panel report on virtual worlds_FR

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European Citizens’Panel on Virtual Worlds (juillet 2023)

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European Citizens’Panel on Virtual Worlds (juillet 2023)

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Ce simulateur est mis à disposition par la Commission. Les résultats de cette simulation ne fournissent qu'une indication non contraignante. Si une vérification formelle de la viabilité financière par la Commission est requise pour une participation à un projet, la Commission pourra vous contacter pour des informations complémentaires.

Ce test est effectué normalement sur tous les partenaires du consortium, exceptés (§7 de l’appel à propositions) :

les entités publiques (telles que définies par la loi nationale, dont les collectivités publiques, les organisations internationales),

les structures actives dans les secteurs culturels et créatifs dont plus de 50% de leurs ressources financières annuelles proviennent de fonds publics au cours des deux derniers exercices,

si le montant de la subvention individuelle demandée par une organisation n'est pas supérieur à 60 000 euros.

Le test de capacité financière permet de confirmer (ou non) le taux du pré-financement de votre subvention européenne (avance de trésorerie) pour le démarrage de votre projet.

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Rapport de l'Observatoire Européen de l'Audiovisuel sur la transparence des algorithmes (2024)

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Rapport de l'Observatoire Européen de l'Audiovisuel sur la transparence des algorithmes (2024)

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Rapport CE Greening Europe Créative - good environmental practices guide

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Rapport CE Greening Europe Créative - good environmental practices guide

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Commission européenne - Europe Créative - Greening strategy

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Commission européenne - Europe Créative - Greening strategy

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Pour compléter votre expertise par d'autres partenaires européens, consultez la Matching partners & expertise data base de l'appel Innovation Lab 2026

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Pour compléter votre expertise par d'autres partenaires européens, consultez la Matching partners & expertise data base de l'appel Innovation Lab 2026

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Les données de projets candidats à l'appel Innovation Lab 2026 qui souhaitent se faire connaître pour partager leur expertise au niveau sont partagées sur cet base de données.

Les Bureaux Europe Créative ont vérifié au préalable l'éligibilité des structures candidates.

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Innovation Lab 2026 data base (OPEN SOURCES)

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L'EACEA a proposé une session d'information de présentation de l'appel Innovation Lab (en anglais), le mardi 20 janvier 2026 à 10h30

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L'EACEA a proposé une session d'information de présentation de l'appel Innovation Lab (en anglais), le mardi 20 janvier 2026 à 10h30

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European Education and Culture Executive Agency

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Online Info Sessions: Creative Europe-MEDIA and Cross sectoral 2026

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Funding opportunities under the Creative Europe Programme – MEDIA

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Rapport de l'Observatoire européen de l'audiovisuel - l'IA dans le secteur AV (2024)

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Rapport de l'Observatoire européen de l'audiovisuel - l'IA dans le secteur AV (2024)

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IA Act - site interactif pour le 3ème draft du code de bonnes pratiques pour l'IA Générative

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IA Act - site interactif pour le 3ème draft du code de bonnes pratiques pour l'IA Générative

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https://digital-strategy.ec.europa.eu/en/library/third-draft-general-purpose-ai-code-practice-published-written-independent-experts?pk_source=ec_newsroom&pk_medium=email&pk_campaign=Shaping%20Europe%27s%20Digital%20Future%20website%20updates/EN

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SUMMARY TRANSPARENCY COPYRIGHT SAFETY & SECURITY

Disclaimer & Contributions

This website is an unofficial, best-effort service to make the Code of Practice more accessible to all observers and working group participants. It contains the full text of the third draft as well as two FAQs and an Explainer on parts of the Code. The respective Chairs and Vice Chairs have written these to address questions posed by many stakeholders.

While I strive for accuracy, this is not an official legal document. Always refer to the official PDF documents provided by the AI Office for the authoritative version (and read their Q&A). Any discrepancies between this site and the official documents should be considered errors on my part.

To help me improve this site please report issues or submit pull requests on Github and feel free to reach out about anything else via email.

Thanks for your support.

Alexander Zacherl

Opening statement by the Chairs and Vice-Chairs

As the Chairs and Vice-Chairs of the four Working Groups, we hereby present the third draft of the General-Purpose AI Code of Practice under the AI Act (the "Code"). Participants in the Working Groups and observers of the Code of Practice Plenary are welcome to submit written feedback on this draft by Sunday, 30 March 2025, via a dedicated survey shared with them.

We encourage all readers - whether they have engaged with previous drafts or not - to visit this website. It contains the text of this third draft as well as two FAQs and an Explainer on parts of the code and is aimed at making the Code more accessible to all observers and working group participants.

The third draft significantly advances the content compared to the second draft. In the upcoming final drafting round, it will be further improved based on stakeholder feedback. For this third draft, we have focused primarily on streamlining the structure of the Code, providing clarifications, adding essential details, and simplifying the Code.

This third draft of the Code addresses key considerations for providers of general-purpose AI models and providers of general-purpose AI models with systemic risk when complying with Chapter V of the AI Act, through four Working Groups working in close collaboration:

Working Group 1: Transparency and copyright-related rules

Working Group 2: Risk assessment for systemic risk

Working Group 3: Technical risk mitigation for systemic risk

Working Group 4: Governance risk mitigation for systemic risk

Working Group 1 Transparency applies to all general-purpose AI models, except for those that are released under a free and open-source licence satisfying the conditions specified in Article 53(2) AI Act and not classified as general-purpose AI models with systemic risk. Working Group 1 Copyright applies to all general-purpose AI models. Working Groups 2, 3, and 4 ( Safety and Security Section) only apply to providers of general-purpose AI models classified as general-purpose AI models with systemic risk based on Article 51 AI Act.

Following a thorough review of the feedback received from stakeholders on the second draft, we have refined Commitments and Measures while maintaining the Code's Objectives. We present this third draft as the basis for the final drafting round, in which we will again draw on your feedback provided via the EU survey, in provider workshops, and in Working Group meetings. Like in previous drafting rounds, we have found your feedback extremely helpful, resulting in substantial changes. We therefore encourage stakeholders to continue providing comprehensive feedback on all aspects of the Code, including both new and unchanged elements. Your feedback will help shape the final version of the Code, which will play a crucial role in guiding the future of general-purpose AI model development and deployment.

We have once again included a high-level drafting plan which outlines our guiding principles for the Code, and the assumptions it is based on.

The AI Act came into force on 1 August 2024, stating that the final version of the Code should be ready by 2 May 2025. The third draft builds upon previous work while aiming to provide a "future-proof" Code, appropriate for the next generation of models which will be developed and released in 2025 and thereafter.

In formulating this third draft, we have been principally guided by the provisions in the AI Act as to matters within the scope of the Code. Accordingly, unless the context and definition contained within the Code indicates otherwise, the terms used in the Code refer to identical terms from the AI Act.

Like the first and second drafts, this document is the result of a collaborative effort involving hundreds of participants from across industry, academia, and civil society. It has been informed by three rounds of feedback, including on the previous two drafts, which has been insightful and instructive in our drafting process. We continue to be informed by the evolving literature on AI governance, international approaches (as specified in Article 56(1) AI Act), industry best practice, and the expertise and experience of providers and Working Group members.

Key features of the development process of the Code include:

Drafted by Chairs and Vice-Chairs who were selected by the AI Office for their expertise, experience, independence (including absence of financial interests), and to ensure gender and geographic diversity.

A multi-stakeholder consultation which closed in September and received 427 submissions

A multi-stakeholder survey on the first draft of the Code which received 354 submissions, and on the second draft which received 336 submissions

Provider workshops led by Chairs and Vice-Chairs

Four specialised working groups led by Chairs and Vice-Chairs

Meetings with representatives from EU Member States in the AI Board and from the European Parliament

Additional time for consultation and deliberation - both externally and internally - will be needed to further improve the Code. As a group of independent Chairs and Vice-Chairs, we strive to make this process as transparent and accessible to stakeholders as possible, aiming to share our work and our thinking as early as possible, while taking sufficient time to coordinate and discuss key questions within Working Groups. We count on your continued engaged collaboration and constructive criticism.

We welcome written feedback by the Code of Practice Plenary participants and observers by Sunday, 30 March 2025, via a dedicated survey shared with them.

Thank you for your support!

Working Group 1, Transparency
Nuria Oliver (Co-Chair) & Rishi Bommasani (Vice-Chair)

Working Group 1, Copyright
Alexander Peukert (Co-Chair) & Céline Castets-Renard (Vice-Chair)

Working Group 2
Matthias Samwald (Chair), Marta Ziosi & Alexander Zacherl (Vice-Chairs)

Working Group 3
Yoshua Bengio (Chair), Daniel Privitera & Nitarshan Rajkumar (Vice-Chairs)

Working Group 4
Marietje Schaake (Chair), Anka Reuel & Markus Anderljung (Vice-Chairs)

Drafting plan, principles, and assumptions

This third draft provides a more streamlined structure with more nuanced Commitments and Measures. In the upcoming final drafting round, it will be further improved based on stakeholder feedback. At this stage, it still does not contain the level of clarity and coherence that we expect in the final adopted version of the Code.

The Code first outlines the Commitments. Concretely, these are 2 Commitments for providers of general-purpose AI models and further 16 Commitments only for providers of general-purpose AI models classified as general-purpose AI models with systemic risk. Next, in separate documents, the Commitments are detailed out with respective Measures. The draft does not include KPIs and instead sharpened the reporting Commitments. Stakeholders should not expect the final adopted version of the Code to contain KPIs.

Related to transparency, Chairs have included a user-friendly Model Documentation Form which allows Signatories to easily document the necessary information in a single place. With regards to the review and adaptation of the Code, this draft includes an Appendix 2 with recommendations to the AI Office.

Below are some high-level principles we follow when drafting the Code:

1. Alignment with EU Principles and Values - Commitments and Measures will be in line with general principles and values of the Union, as enshrined in EU law, including the Charter of Fundamental Rights of the European Union, the Treaty on European Union and Treaty on the Functioning of the European Union.

2. Alignment with the AI Act and International Approaches - Commitments and Measures will contribute to a proper application of the AI Act. This includes taking into account international approaches (including standards or metrics developed by AI Safety Institutes, or standard-setting organisations), in accordance with Article 56(1) AI Act.

3. Proportionality to Risks - Commitments and Measures should be proportionate to risks, meaning they should be (i) suitable to achieve the desired end, (ii) necessary to achieve the desired end, and (iii) should not impose a burden that is excessive in relation to the end sought to be achieved. Some concrete applications of proportionality include:

1. Commitments and Measures should be more stringent for higher risk tiers or uncertain risks of severe harm.

2. Measures should be specific. While Commitments may be articulated at a higher level of generality, general-purpose AI model providers should have a clear understanding of how to meet Measures. Measures should be designed to be effective and robust against misspecification or any attempts of circumvention. The Code strives to accomplish this by, for example, avoiding unnecessary use of proxy terms or metrics. The AI Office will monitor and review Measures that may be susceptible to circumvention and other forms of misspecification.

3. Commitments and Measures should differentiate, where applicable, between different types of risks, distribution strategies and deployment contexts of the concerned general-purpose AI model, and other factors that may influence the tiers of risk, and how risks need to be assessed and mitigated. For example, Commitments and Measures assessing and mitigating systemic risks might need to differentiate between intentional and unintentional risks, including instances of misalignment. Additionally, Commitments may need to be adapted to take into account the different tools providers have available to assess and mitigate systemic risk where model weights are freely released.

4. Future-Proof - AI technology is changing rapidly. Measures should maintain the AI Office's ability to improve its assessment of compliance based on new information. Therefore, the Code shall strive to facilitate its rapid updating, as appropriate. It is important to find a balance between specific commitments on one hand, and the flexibility to update rapidly in light of technological and industry developments on the other. The Code can accomplish this by, for example, referencing dynamic sources of information that providers can be expected to monitor and consider in their risk assessment and mitigation. Examples of such sources could include incident databases, consensus standards, up-to-date risk registers, state-of-the-art risk management frameworks, and AI Office guidance. As technology evolves, it may also be necessary to articulate an additional set of Measures for specific general-purpose AI models, for example, certain models used in agentic AI systems.

5. Proportionality to the size of the general-purpose AI model provider - Measures related to the obligations applicable to providers of general-purpose AI models should take due account of the size of the general-purpose AI model provider and allow simplified ways of compliance for small and medium enterprises (SMEs) and start-ups with fewer financial resources than those at the frontier of AI development, where appropriate.

6. Support and growth of the ecosystem for safe, human centric and trustworthy AI - We recognise that the development, adoption, and governance of general-purpose AI models are global issues. Many Commitments in this draft are intended to enable and support cooperation between different stakeholders, for example by sharing general-purpose AI safety infrastructure and best practices amongst model providers, or by encouraging the participation of civil society, academia, third parties, and government organisations in evidence collection. We promote further transparency between stakeholders and increased efforts to share knowledge and cooperate in building a collective and robust evidence base for safe, human centric and trustworthy AI in line with Article 56(1) and (3), Recital 1, and Recital 116 AI Act. We also acknowledge the positive impact that open-source models have had on the development of safe, human centric and trustworthy AI.

7. Innovation in AI governance and risk management - We recognise that determining the most effective methods for understanding and ensuring the safety of general-purpose AI models remains an evolving challenge. The Code should encourage providers to compete in and advance the state-of-the-art in AI safety governance and related evidence collection methods and practices. When providers can demonstrate equal or superior safety outcomes through alternative approaches that are less burdensome, these innovations should be recognised as improving the state of the art of AI governance and evidence and we should support their wider adoption.

The current draft is written with the assumption that there will only be a small number of both general-purpose AI models with systemic risk and providers thereof. That assumption seems to be confirmed by the information provided from the AI Office accompanying the publication of this draft. The AI Office plans to publish guidance in due time to clarify the scope of the respective AI Act rules in proximity to the publication of the final Code of Practice, including topics addressed in the dedicated Q&A such as downstream modifiers to which obligations should only apply in clearly specified cases. In particular, we want to highlight that even if modifications of general-purpose AI models increase the number of providers in scope, the modifiers' obligations under Articles 53 and 55 AI Act should be limited to the extent of their respective modifications, as appropriate. We expect more clarifications from the AI Office on these points on an ongoing basis, as stated in the Q&A.

Preamble

The Objectives of the Code are as follows:

I. Commitments by Providers of General-Purpose AI Models

Transparency Section

Commitment I.1. Documentation

In order to fulfil the obligations in Article 53(1), points (a) and (b) AI Act, Signatories commit to drawing up and keeping up-to-date model documentation in accordance with Measure I.1.1, providing relevant information to providers of AI systems who intend to integrate the general-purpose AI model into their AI systems (downstream providers hereafter), and to the AI Office upon request (possibly on behalf of national competent authorities when this is strictly necessary for the exercise of their supervisory tasks under the AI Act, in particular to assess the compliance of high-risk AI systems built on general-purpose AI models where the provider of the system is different from the provider of the model [Footnote: See Article 75(1) and (3) AI Act and Article 88(2) AI Act.]), in accordance with Measure I.1.2, and ensuring quality, security, and integrity of the documented information in accordance with Measure I.1.3. These Measures do not apply to providers of open-source AI models satisfying the conditions specified in Article 53(2) AI Act, unless the models are general-purpose AI models with systemic risk.
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Copyright Section

Commitment I.2. Copyright policy

In order to fulfil the obligation to put in place a policy to comply with Union law on copyright and related rights, and in particular to identify and comply with, including through state-of-the-art technologies, a reservation of rights expressed pursuant to Article 4(3) Directive (EU) 2019/790 pursuant to Article 53(1), point (c) AI Act, Signatories commit to drawing up, keeping up-to-date, and implementing a copyright policy in accordance with Measure I.2.1, as well as adopting Measures I.2.2-I.2.6 for their general-purpose AI models placed on the EU market.
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II. Commitments by Providers of General-Purpose AI Models with Systemic Risk

Safety and Security Section

Commitment II.1. Safety and Security Framework

Signatories commit to adopting and implementing a Safety and Security Framework (hereafter, "the Framework") that will: (1) apply to the Signatories' GPAISRs; and (2) detail the systemic risk assessment, systemic risk mitigation, and governance risk mitigation measures and procedures that Signatories intend to adopt to keep systemic risks stemming from their GPAISRs within acceptable levels.
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Commitment II.2. Systemic risk assessment and mitigation along the entire model lifecycle, including during model development

Signatories commit to conducting systemic risk assessment systematically at appropriate points along the entire model lifecycle, in particular before making the model available on the market. Specifically, Signatories commit to starting to assess and mitigate systemic risks during the development of a GPAISR, as specified in the Measures for this Commitment.
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Commitment II.3. Systemic risk identification

Signatories commit to selecting and further characterising systemic risks stemming from their GPAISRs that are significant enough to warrant further assessment and mitigation, as specified in the Measures for this Commitment.
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Commitment II.4. Systemic risk analysis

As part of systemic risk assessment, Signatories commit to carrying out a rigorous analysis of the systemic risks identified pursuant to Commitment II.3 in order to understand the severity and probability of the systemic risks. Signatories commit to carrying out systemic risk analysis with varying degrees of depth and intensity, as appropriate to the systemic risk stemming from the relevant GPAISR and as specified in the Measures for this Commitment. Whenever systemic risk mitigations are implemented, Signatories commit to considering their effectiveness and robustness as part of systemic risk analysis.

As further specified in the Measures for this Commitment, Signatories commit to making use of a range of information and methods in their systemic risk analysis including model-independent information and state-of-the-art model evaluations, taking into account model affordances, safe originator models, and the context in which the model may be made available on the market and/or used and its effects.
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Commitment II.5. Systemic risk acceptance determination

Signatories commit to determining the acceptability of the systemic risks stemming from their GPAISRs by comparing the results of their systemic risk analysis (pursuant to Commitment II.4) to their pre-defined systemic risk acceptance criteria (pursuant to Measure II.1.2), in order to ensure proportionality between the systemic risks of the GPAISR and their mitigations. Signatories commit to using this comparison to inform the decision of whether or not to proceed with the development, the making available on the market, and/or the use of their GPAISR, as specified in the Measures for this Commitment.
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Commitment II.6. Safety mitigations

Signatories commit, as specified in the Measures for this Commitment, to: (1) implementing technical safety mitigations along the entire model lifecycle that are proportionate to the systemic risks arising from the development, the making available on the market, and/or the use of GPAISRs, in order to reduce the systemic risks of such models to acceptable levels, and further reduce systemic risk as appropriate, in accordance with this Code; and (2) ensuring that safety mitigations are proportionate and state-of-the-art.
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Commitment II.7. Security mitigations

Signatories commit to mitigating systemic risks that could arise from unauthorised access to unreleased model weights of their GPAISRs and/or unreleased associated assets. Associated assets encompass any information critical to the training of the model, such as algorithmic insights, training data, or training code.

Consequently, Signatories commit to implementing state-of-the-art security mitigations designed to thwart such unauthorised access by well-resourced and motivated non-state-level adversaries, including insider threats from humans or AI systems, so as to meet at least the RAND SL3 security goal or equivalent, and achieve higher security goals (e.g. RAND SL4 or SL5), as specified in the Measures for this Commitment.
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Commitment II.8. Safety and Security Model Reports

Signatories commit to reporting to the AI Office about their implementation of the Code, and especially the application of their Framework to the development, making available on the market, and/or use of their GPAISRs, by creating a Safety and Security Model Report (hereafter, a "Model Report") for each GPAISR which they make available on the market, which will document, as specified in the Measures for this Commitment: (1) the results of systemic risk assessment and mitigation for the model in question; and (2) justifications of decisions to make the model in question available on the market.
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Commitment II.9. Adequacy assessments

Signatories commit to assessing the adequacy of their Framework, the adoption and implementation of which they have committed to under Commitment II.1, and to updating it based on the findings as specified in the Measures for this Commitment.
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Commitment II.10. Systemic risk responsibility allocation

For activities concerning systemic risk assessment and mitigation for their GPAISRs, Signatories commit, as specified in the Measures for this Commitment, to: (1) clearly defining and allocating responsibilities for managing systemic risk from their GPAISRs across all levels of the organisation; (2) allocating appropriate resources to actors who have been assigned responsibilities for managing systemic risk; and (3) promoting a healthy risk culture.

Signatories commit to allocating appropriate levels of responsibility and resources proportionately to, at least, the Signatory's organisational complexity and governance structure, and the systemic risks stemming from their GPAISRs.
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Commitment II.11. Independent external assessors

Before placing a GPAISR on the market, Signatories commit to obtaining independent external systemic risk assessments, including model evaluations, unless the model can be deemed sufficiently safe, as specified in Measure II.11.1. After placing the GPAISR on the market, Signatories commit to facilitating exploratory independent external assessments, including model evaluations, as specified in Measure II.11.2.
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Commitment II.12. Serious incident reporting

Signatories commit, to the extent and under the conditions specified in Measures II.12.1 to II.12.4, to setting up processes for keeping track of, documenting, and reporting to the AI Office and, as appropriate, to national competent authorities without undue delay relevant information about serious incidents throughout the entire model lifecycle and possible corrective measures to address them, with adequate resourcing of such processes relative to the severity of the serious incident and the degree of involvement of their model.
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Commitment II.13. Non-retaliation protections

Signatories commit to not retaliating against any worker providing information about systemic risks stemming from the Signatories' GPAISRs to the AI Office or, as appropriate, to national competent authorities, and to at least annually informing workers of an AI Office mailbox designated for receiving such information, if such a mailbox exists.
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Commitment II.14. Notifications

Signatories commit, as specified in the Measures for this Commitment, to: (1) notifying the AI Office of relevant information regarding their general-purpose AI models meeting the condition for classification as GPAISRs; and (2) regularly notifying the AI Office of the implementation of the Commitments and Measures of this Code. For the purpose of assessing the implementation of this Code through the AI Office, Signatories commit to offering clarifications, including via further documentation or interviews, where requested by the AI Office.
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Commitment II.15. Documentation

Signatories commit to documenting relevant information under the AI Act and the Code, as specified in Measure II.15.1.
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Commitment II.16. Public transparency

Signatories commit to publishing information relevant to the public understanding of systemic risks stemming from their GPAISRs, where necessary to effectively enable assessment and mitigation of systemic risks, to the extent and under the conditions specified in Measure II.16.1.
Read commitment and measures The foregoing Commitments are supplemented by Measures found in the relevant Transparency, Copyright or Safety and Security section in the separate accompanying documents. [Note: On this site shown as seperate sub-pages.]

Introductory note by the Chair and Vice-Chair of the Transparency Section

Frequently Asked Questions: Transparency

This FAQ section provides additional explanatory information written and shared by the (vice) chairs for WG1-Transparency. In case of any discrepancies between this section and the legal text of the code, the legal text always takes precedence.

The Transparency section of the Code of Practice describes three Measures which Signatories commit to taking to comply with their transparency obligations under Article 53(1)(a) and (b) and the corresponding Annexes XI and XII AI Act.

In this third draft, to streamline fulfilment of the commitments contained in Measure I.1.1 and facilitate Signatories' compliance, we have included a user-friendly Model Documentation Form which allows Signatories to easily document the necessary information in a single place.

TThe Form clearly indicates for each item whether it is intended for downstream providers, the AI Office or national competent authorities. Whilst information intended for downstream providers should be made available to them proactively, information intended for the AI Office or national competent authorities is only to be made available following a request from the AI Office, either ex officio or based on a request to the AI Office from national competent authorities. Such requests will state the legal basis and purpose of the request and will concern only items from the Form strictly necessary for the AI Office to fulfil its tasks under the AI Act at the time of the request, or for national competent authorities to exercise their supervisory tasks under the AI Act at the time of the request, in particular to assess compliance of high-risk AI systems built on general-purpose AI models where the provider of the system is different from the provider of the model.

Finally, in accordance with Article 78 AI Act, the recipients of any of the information contained in the Model Documentation Form are obliged to respect the confidentiality of the information obtained, in particular intellectual property rights and confidential business information or trade secrets, and to put in place adequate and effective cybersecurity measures to protect the security and confidentiality of the information obtained.

Working Group 1, Transparency
Nuria Oliver (Co-Chair) & Rishi Bommasani (Vice-Chair)

Recitals for the Transparency Section
Whereas:

1. The Signatories recognise the particular role and responsibility of providers of general-purpose AI models along the AI value chain, as the models they provide may form the basis for a range of downstream systems, often provided by downstream providers that need significant understanding of the models and their capabilities, both to enable the integration of such models into their products and to fulfil their obligations under the AI Act (see Recital 101 AI Act).

2. The Signatories recognise that in the case of a modification or fine-tuning of a model, the obligations for providers should be limited to that modification or fine-tuning to safeguard proportionality (see Recital 109 AI Act).

Commitment I.1 Documentation

In order to fulfil the obligations in Article 53(1), points (a) and (b) AI Act, Signatories commit to drawing up and keeping up-to-date model documentation in accordance with Measure I.1.1, providing relevant information to providers of AI systems who intend to integrate the general-purpose AI model into their AI systems (downstream providers hereafter), and to the AI Office upon request (possibly on behalf of national competent authorities upon request to the AI Office when this is strictly necessary for the exercise of their supervisory tasks under the AI Act, in particular to assess compliance of high-risk AI systems built on general-purpose AI models where the provider of the system is different from the provider of the model [Footnote: See Article 75(1) and (3) AI Act and Article 88(2) AI Act.]), in accordance with Measure I.1.2, and ensuring quality, security, and integrity of the documented information in accordance with Measure I.1.3. These Measures do not apply to providers of open-source AI models satisfying the conditions specified in Article 53(2) AI Act, unless the models are general-purpose AI models with systemic risk.

Measure I.1.1. Drawing up and keeping up-to-date model documentation

Signatories, when placing a general-purpose AI model on the market, commit to having prepared a document entitled "Information and Documentation about the General-Purpose AI Model" (hereafter Model Documentation) containing all the information referred to in the Model Documentation Form below.

Signatories commit to reporting the information requested in the Computational Resources and Energy Consumption sections in consistency with any delegated act adopted in accordance with Article 53(5) AI Act to detail measurement and calculation methodologies with a view to allowing for comparable and verifiable documentation.

In case of relevant changes in the information contained in the Model Documentation, Signatories commit to update the Model Documentation to reflect the new information while keeping previous versions of the Model Documentation for a period ending 10 years after the model has been placed on the market.

Measure I.1.2. Providing relevant information

Signatories, when placing a general-purpose AI model on the market, commit to publicly disclosing via their website, or via another means if they do not have a website, contact information for the AI Office and downstream providers to request access to the relevant information contained in the Model Documentation.

Upon a request from the AI Office pursuant to Articles 91 or 75(3) AI Act for one or more elements of the Model Documentation that are strictly necessary for the AI Office to fulfil its tasks under the AI Act or for national competent authorities to exercise their supervisory tasks under the AI Act, in particular to assess compliance of high-risk AI systems built on general-purpose AI models where the provider of the system is different from the provider of the model [Footnote: See Article 75(1) and (3) AI Act and Article 88(2) AI Act.], Signatories commit to providing the relevant elements contained in the most up-to-date Model Documentation, or otherwise the necessary additional information, subject to the confidentiality safeguards and conditions provided for under Articles 53(7) and 78 AI Act.

Signatories commit to providing to downstream providers the information contained in the most up-to-date Model Documentation and intended for downstream providers, subject to the confidentiality safeguards and conditions provided for under Articles 53(7) and 78 AI Act. Furthermore, subject to the same confidentiality safeguards and conditions, Signatories commit to providing additional information necessary to enable downstream providers to have a good understanding of the capabilities and limitations of the general-purpose AI model and to comply with their obligations pursuant to the AI Act.

Signatories commit to taking all the above-described actions in a timely manner.

Signatories are encouraged to consider whether the documented information can be disclosed, in whole or in part, to the public to promote public transparency. Some of this information may also be required in a summarised form as part of the public summary for training content that providers must make publicly available under Article 53(1), point (d) AI Act to be determined in a template to be provided by the AI Office.

Measure I.1.3. Ensuring quality, integrity, and security of information

Signatories commit to ensuring that the documented information is controlled for quality and integrity, retained as evidence of compliance with obligations of the AI Act, and protected from unintended alterations. In the context of drawing-up, updating, and controlling the quality and security of the information and records, Signatories are encouraged to follow the established protocols and technical standards.

Model Documentation Form

Below is a static, non-editable version of the Model Documentation Form. In this version, the input fields cannot be filled in, and the button at the bottom of the form-intended to generate a version of the form containing only information intended for downstream providers-is non-functional. In the final draft of the Code, this Form will be fully interactive and editable

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Introductory note by the Chair and Vice-Chair of the Copyright Section

The Copyright section of the Code of Practice describes a set of Measures that Signatories commit to taking in order to comply with their obligation under Article 53(1)c) AI Act. The third draft retains core elements of the first two drafts in a simplified and clearer form. Measure I.2.1 (former Measures 2.1 and 2.2, Draft 2) specifies what it means to "put in place" a policy to comply with Union copyright law and clarifies the set of Measures covered. While Measures I.2.2 and I.2.3 (former Measures 2.4-2.8, Draft 2) regulate the mining of web-crawled content, Measure I.2.4 (former Measure 2.3) concerns the mining of protected content not web-crawled by the Signatory. Measure I.2.5 (former Measures 2.9 and 2.10, Draft 2) sets out commitments to mitigate the risk that a downstream AI system repeatedly generates infringing output. Finally, Measure I.2.6 (former Measure 2.11, Draft 2) provides for commitments to designate a point of contact and to allow for the submission of complaints concerning the non-compliance of Signatories with their commitments under the copyright Section. As indicated in the General Introduction, this Section no longer contains separate KPIs. However, the features of some KPIs have been incorporated into the Measures (e.g. description of the policy in a single document, easily accessible information on the contact point and the possibility to lodge complaints). The recitals clarify that this Section is without prejudice to the application and enforcement of Union law on copyright and related rights and to commercial agreements between the Signatories and rightsholders authorising the use protected content.

Working Group 1, Copyright
Alexander Peukert (Co-Chair) & Céline Castets-Renard (Vice-Chair)

Recitals for the Copyright Section
Whereas:
Commitment I.2. Copyright policy

In order to fulfil the obligation to put in place a policy to comply with Union law on copyright and related rights, and in particular to identify and comply with, including through state-of-the-art technologies, a reservation of rights expressed pursuant to Article 4(3) of Directive (EU) 2019/790 pursuant to Article 53(1), point (c) AI Act, Signatories commit to drawing up, keeping up-to-date, and implementing a copyright policy in accordance with Measure I.2.1, as well as adopting Measures I.2.2-I.2.6 for their general-purpose AI models placed on the EU market.

Measure I.2.1. Draw up, keep up-to-date and implement a copyright policy

1. Signatories will draw up, keep up-to-date and implement a policy to comply with Union law on copyright and related rights. This policy will address all commitments pursuant to this Section and will be described in a single document approved by the Signatory. Signatories will assign responsibilities within their organisation for the implementation and overseeing of this policy.

2. Signatories are encouraged to make publicly available and keep up-to-date a summary of their copyright policy.

Measure I.2.2. Reproduce and extract only lawfully accessible copyright-protected content when crawling the World Wide Web

In order to ensure that Signatories will only reproduce and extract lawfully accessible works and other protected subject matter if they use web-crawlers or have such web-crawlers be used on their behalf to crawl, scrape and/or otherwise compile data for the purpose of text and data mining according to Article 2(2) of Directive (EU) 2019/790 and the training of their general-purpose AI models, Signatories will:

1. not circumvent effective technological measures as defined in Article 6(3) of Directive (EC) 2001/29 designed to prevent or restrict access to works and other protected subject matter, such as paywalls, and

2. make reasonable efforts to exclude from their web-crawling Internet domains that make available to the public copyright-infringing content on a commercial scale and have no substantial legitimate uses ("piracy domains") and that are recognised as such by courts or public authorities in the European Union and the European Economic Area. A list of hyperlinks to relevant piracy domain lists issued by the relevant bodies in the European Union and the European Economic Area is to be made publicly available on an EU website.

Measure I.2.3. Identify and comply with rights reservations when crawling the World Wide Web

1. In order to ensure that Signatories will identify and comply with, including through state-of-the-art technologies, machine-readable reservations of rights expressed pursuant to Article 4(3) of Directive (EU) 2019/790 if they use web-crawlers or have such web-crawlers used on their behalf to crawl, scrape and/or otherwise compile data for the purpose of text and data mining according toArticle 2(2) of Directive (EU) 2019/790 and the training of their general-purpose AI models, Signatories will

1. employ web-crawlers that read and follow instructions expressed in accordance with the Robot Exclusion Protocol (robots.txt), as specified in the Internet Engineering Task Force (IETF) Request for Comments No. 9309, and any subsequent version of this IETF standard, and

2. make best efforts to identify and comply with other appropriate machine-readable protocols to express rights reservations pursuant to Article 4(3) of Directive (EU) 2019/790, for example through asset-based or location-based metadata, that have either resulted from a cross-industry standard-setting process as referred to in paragraph 3 of this Measure or are state-of-the-art and widely adopted by rightsholders, considering different cultural sectors, and generally agreed through an inclusive process based on bona fide discussions to be facilitated at EU level with the involvement of rightsholders, AI providers and other relevant stakeholders as a more immediate solution, while anticipating the development of cross-industry standards referred in paragraph 3.

2. This commitment is without prejudice to the right of rightsholders to expressly reserve the use of lawfully accessible works and other protected subject matter for the purposes of text and data mining pursuant to Article 4(3) of Directive (EU) 2019/790 in any appropriate manner, such as machine-readable means in the case of content made publicly available online.

3. In due consideration of relevant international and European standard-setting processes, Signatories are encouraged to support relevant standardisation efforts and engage on a voluntary basis in bona fide discussions with other relevant stakeholders, including rightsholders, with the aim to develop appropriate machine-readable standards to express a rights reservation pursuant Article 4(3) of Directive (EU) 2019/790.

4. Signatories will take reasonable measures to enable affected rightsholders to obtain information about the web crawlers employed and their robot.txt features and the measures that a Signatory adopts to identify and comply with rights reservations expressed pursuant to Article 4(3) of Directive (EU) 2019/790 at the time of crawling, for example by making public such information and syndicating a web feed that covers every update of the website informing about the rights reservation compliance.

5. Signatories that also provide an online search engine as defined in Article 3(j) Regulation (EU) 2022/2065 or control such a provider are encouraged to take appropriate measures to ensure that its compliance with a rights reservation expressed in accordance with paragraph 1 of this Measure does not negatively affect the findability of the content, for which a rights reservation has been expressed, through their search engine.

Measure I.2.4. Obtain adequate information about protected content not web-crawled by the Signatory

1. If Signatories, for the training of their general-purpose AI models, mine works and other protected subject matter according to Article 2(2) of Directive (EU) 2019/790 that they have obtained by other means than by web-crawling or by web-crawling on their behalf and without an authorisation by the respective rightsholders or their authorised representatives, they will make reasonable efforts to obtain adequate information (e.g., by checking the information available on the website of the third parties or requesting information) as to whether works and other protected subject matter that have been scraped or crawled from the internet were collected by employing web-crawlers that read and follow instructions expressed in accordance with the Robot Exclusion Protocol (robots.txt), as specified in the Internet Engineering Task Force (IETF)Request for Comments No. 9309, and any subsequent version of this IETF standard.

2. This Measure does not imply a commitment to verify or proceed to a work-by-work assessment of the data mentioned in paragraph 1 of this Measure in terms of copyright compliance.

Measure I.2.5. Mitigate the risk of production of copyright-infringing output

1. In order to mitigate the risk that a downstream AI system, into which a generative general-purpose AI model is integrated, repeatedly generates output that infringes copyrights or related rights as protected according to Union law on copyright and related rights, Signatories will

1. make reasonable efforts to mitigate the risk that a model memorizes copyrighted training content to the extent that it repeatedly produces copyright-infringing outputs and

2. prohibit copyright-infringing uses of a model in their acceptable use policy, terms and conditions, or other equivalent documents.

2. This Measure applies irrespective of whether a Signatory vertically integrates the model into its own AI system(s) or whether the model is provided to another entity based on contractual relations. The commitment to prohibit copyright-infringing uses pursuant to paragraph 1, point b of this Measure does not apply to general-purpose AI models that are released under a free and open-source licence.

Measure I.2.6. Designate a point of contact and enable the lodging of complaints

1. Signatories will designate a point of contact for communication with affected rightsholders and provide easily accessible information about it.

2. Signatories will put a mechanism in place to allow affected rightsholders and their authorised representatives, including collective management organisations, to submit, by electronic means, sufficiently precise and adequately substantiated complaints concerning the non-compliance of Signatories with their commitments pursuant to this Section and provide easily accessible information about it. Where complaints by rightsholders are manifestly unfounded or excessive, in particular because of their repetitive character, Signatories may refuse to act on the complaint.

Commitments by Providers of GPAISRs

Frequently Asked Questions: Safety & Security
This FAQ, written and shared by the (vice) chairs of Working Groups 2-4, aims to provide answers to some questions that the chairs frequently receive about the part of the Code of Practice that pertains to general-purpose AI models with systemic risk (GPAISRs), also called the "Safety and Security Section" of the Code. It is intended to support readers' understanding of the Code of Practice; if and to the extent that this FAQ contradicts the Code, the Code takes precedence.

Explainer: About the Safety and Security Section of the Code
This explainer, written by the (Vice) Chairs of Working Groups 2-4, is intended to give readers a high-level understanding of the Safety and Security Section of the Code for general-purpose AI models (GPAI). If and to the extent that this explainer contradicts the Code, the Code takes precedence.
The Safety and Security Section of the Code of Practice describes one way in which leading AI companies can comply with the AI Act. The AI Act is a binding AI regulation passed by the European Union in 2024. The part of the AI Act that is concerned with obligations for general-purpose AI models (GPAI) will become effective on August 2, 2025. To give GPAI providers a blueprint of one way to comply with these obligations, the European Commission appointed a group of independent AI experts with backgrounds in industry, academia, and policymaking to lead the drafting of a GPAI Code of Practice ("the Code"). The drafting of the Code is an iterative process that benefits from repeated input from companies and other stakeholders and has now seen the publication of the third and penultimate draft. This explainer is about the Safety and Security Section of the Code, which specifies a way of complying with one subset of the AI Act's GPAI obligations: the obligations for "GPAI with Systemic Risk" (GPAISR).

To make AI Act compliance as easy as possible, the Safety and Security Section of the Code translates the Act's obligations for leading companies into a framework structure that many of them are already using. This type of risk management framework is already used by several AI companies. At the time of writing, AI companies that have published their version of such a framework include Anthropic, OpenAI, Google DeepMind, Meta, Cohere, Microsoft, Amazon, xAI, and NVIDIA. Several other companies, including Inflection AI, Mistral AI, and Zhipu AI, have not yet published their framework but have publicly committed to doing so. While these frameworks have different names in different companies and differ in some respects, their basic structure is the same: companies define thresholds beyond which they consider risk stemming from their models to be unacceptable, and describe how they intend to identify, assess, and mitigate risk to keep it below those thresholds. The goal of the Safety and Security Section of the Code is to specify a way in which the following can be sufficient for demonstrating compliance with the AI Act's obligations concerning GPAISR: (1) have such a framework in place that meets certain basic criteria; and (2) actually stick to the framework.

The Safety and Security Section of the Code is only relevant to the very small number of AI companies that are subject to the AI Act's obligations concerning GPAI that presents "systemic risk". The AI Act defines systemic risk as being specific to capabilities that match or exceed the capabilities of the most advanced GPAI. The Act's obligations for GPAISR should therefore only apply to a very small number of companies. The drafters of the Safety and Security Section of the Code have no discretion over how the regulator will interpret the GPAISR definition in practice, but have been writing the Safety and Security Section assuming that only about 5-15 companies will be in scope of the AI Act's GPAISR obligations at any given point in time. Further, the drafters expect models obtained by fine-tuning an existing GPAISR to fall outside the scope of the AI Act's obligations for GPAISR; or, if they are in scope, the drafters recommend that such obligations are not enforced against such models produced by fine-tuning, until there are reasons to believe that such fine-tuning could introduce novel and unacceptable risk. The drafters expect the European AI Office to publish guidance on this question soon.

The Safety and Security Section of the Code reduces uncertainty and streamlines risk management requirements in order to enable innovation while ensuring public safety. The purpose of the Safety and Security Section is to make compliance with the AI Act's obligations related to GPAISR easier. While the AI Act lists about 40 different examples of potential systemic risks, the drafters of the Safety and Security Section take the AI Act's definition of "systemic risk" to only clearly apply to 4 kinds of risks: (1) cyber offence risk; (2) chemical, biological, radiological and nuclear (CBRN) risk; (3) harmful manipulation risk; and (4) loss of control risk. Companies adhering to the Safety and Security Section of the Code would always assess and potentially mitigate these selected systemic risks. Regarding other potential systemic risks that can be reasonably foreseen, companies would at least consider such risks as part of their systemic risk selection process.

The 4 kinds of risks specified within the Safety and Security Section of the Code could each lead to catastrophic outcomes and threaten the health, safety, and fundamental rights of many people. Scientific studies have found increasing evidence of highly capable GPAI

1. discovering vulnerabilities in computer code (includingpreviously unknown ones);

2. lowering barriers to biological and chemical weapons development for both novices and experts;

3. being capable of effective, targeted psychological manipulation; and

4. trying to resist being shut down in certain circumstances and generating answers thatfalsely suggest alignment with goals provided by its developers.

In addition to leading independent experts, senior company leaders have also repeatedly warned of these kinds of risks. Recently, one AI company increased its assessments of CBRN risk from its previously best model from 'low' to 'medium', and stated in an even more recent release that "our models are on the cusp [...] of cross[ing] our high risk threshold".

The Safety and Security Section of the Code is newcomer-friendly and designed to boost adoption of highly capable GPAISR across the economy. As described above, the drafters expect the Safety and Security Section to only be relevant to 5-15 companies at any given point in time. Often, these companies are large, well-resourced incumbents. But in some cases, "newcomer" companies might also be subject to the AI Act's GPAISR obligations. The Safety and Security Section of the Code is designed to make compliance easier for them: they can use shared methods for risk assessment and have, in many cases, lighter documentation requirements. Further, the Safety and Security Section aims to boost adoption of highly capable GPAISRs across the economy. Many companies in various sectors want to use such GPAISRs in their products and services - but need those GPAISRs to be reliable and safe in order to do so. The Safety and Security Section, by outlining risk management practices for providers of GPAISR, can thus help create the necessary trust for highly capable GPAI to be applied across the economy and help boost innovation.

Given the rapid pace at which GPAI capabilities are advancing, the Safety and Security Section of the Code has been written to be as future proof as possible. Companies deciding to adhere to our current draft would have a lot of discretion over how to adhere to it. For example, they themselves choose what mitigations to put in place to keep risk to an acceptable level. Further, our draft refers to the state of the art and to supporting input from the AI Office in several places, allowing the implementation of the Safety and Security Section of the Code to adapt with the state of the science.
Summary of the Safety and Security Section
The Safety and Security Section of the Code consists of 4 main parts, with the first part containing a commitment to have a "Safety and Security Framework". This is the type of framework described above that is popular in the industry already. Companies adhering to the Safety and Security Section commit to adopting and implementing a Safety and Security Framework that meets certain basic criteria. For example, the Framework should specify thresholds beyond which that company considers a given systemic risk stemming from their GPAISR unacceptable.

The second part of the Safety and Security Section contains commitments related to risk assessment pursuant to the company's Framework. Companies adhering to the Safety and Security Section commit to identifying and analysing systemic risks along the entire lifecycle of a GPAISR, and to determining whether any systemic risk stemming from the model exceeds the company's pre-defined risk acceptance criteria.

The third part of the Safety and Security Section contains commitments related to risk mitigation pursuant to the company's Framework. Companies adhering to the Safety and Security Section commit to implementing technical safety and security mitigations, along the entire model lifecycle, that are proportionate and state-of-the-art. These mitigations should reduce systemic risks of the model to acceptable levels as defined in the company's framework, and further reduce systemic risks as appropriate.

The fourth part of the Safety and Security Section contains commitments related to organizational structures that support adherence to the company's Framework. Companies adhering to the Safety and Security Section commit to reporting to the European AI Office about their implementation of the Code, and especially the application of their Framework, by creating a "Safety and Security Model Report" for each GPAISR. The Report should document the results of systemic risk assessment and mitigation for the GPAISR, showing that the systemic risk stemming from that model is acceptable. Additional commitments in the fourth part of the Safety and Security Section address, for example, the reporting of serious incidents, and, under certain specified conditions, independent external risk assessment ( see here for more details on external testing).

Introductory note by the Chairs and Vice-Chairs of the Safety and Security Section

We have put together an Explainer document and an FAQ about the Safety and the Security Section, which readers can access on this webpage (see above). We encourage all readers - whether they have engaged with previous drafts or not - to read those documents as they should provide a good overview of what this Section is about.

The Safety and Security Section of the Code of Practice provides clarity to leading AI companies on a way to comply with the AI Act. The AI Act is a binding AI regulation passed by the European Union in 2024. The part of the AI Act that is concerned with obligations for general-purpose AI models (GPAI) will become effective on August 2, 2025. To give GPAI providers a blueprint of one way to comply with these obligations, the European Commission appointed us as part of a group of independent AI experts who have backgrounds in industry, academia, and policymaking to lead the drafting of a GPAI Code of Practice. The drafting of the Code is an iterative process that benefits from repeated input from companies and other stakeholders over multiple drafts. This Safety and Security Section is part of the third and penultimate draft. The Section specifies a way of complying with one subset of the AI Act's GPAI rules: the rules for "GPAI with Systemic Risk" (GPAISR).

We would like to thank all stakeholders for their valuable input and collaborative stance. We welcome written feedback by the Code of Practice Plenary participants and observers by Sunday, 30 March 2025, via a dedicated survey shared with them. We look forward to the next steps of the drafting process.

Working Group 2
Matthias Samwald (Chair), Marta Ziosi & Alexander Zacherl (Vice-Chairs)

Working Group 3
Yoshua Bengio (Chair), Daniel Privitera & Nitarshan Rajkumar (Vice-Chairs)

Working Group 4
Marietje Schaake (Chair), Anka Reuel & Markus Anderljung (Vice-Chairs)

Recitals for the Safety and Security Section
Whereas:

1. The Signatories recognise that providers of GPAISRs should continuously assess and mitigate systemic risks, taking appropriate measures along the entire model lifecycle, cooperating with and taking into account relevant actors along the AI value chain (such as stakeholders likely to be affected by the GPAISR), and ensuring their systemic risk management builds on state-of-the-art measures and is future-proof by regularly updating their practices in light of improving and emerging capabilities (see Recital 114 AI Act). Systemic risk assessment is a multi-stage process and model evaluations, referring to a range of methods used in the assessment of the systemic risks of GPAISRs, are integral throughout. Where systemic risk mitigations are implemented, Signatories recognise the importance of continuously assessing their robustness and adequacy to sufficiently mitigate the targeted systemic risk, following state-of-the-art processes, such as human red-teaming by internal or external assessors; automated red-teaming; and/or bug-bounty programs which incentivise the public to discover and report vulnerabilities in safety mitigations.

2. The Signatories recognise that this Safety and Security Section of the Code only applies to providers of GPAISRs and not AI systems but that the assessment and mitigation of systemic risks should include, as reasonably foreseeable, the system architecture and other software components into which the model may be integrated, as well as the computing resources available at inference time, because of their importance to the model's capabilities and safeguards. In cases where providers of GPAISRs also develop, provide, or deploy AI systems based on GPAISRs, they recognise the importance of taking into account these AI systems for their assessment and mitigation of systemic risks.

3. The Signatories recognise that the degree of scrutiny and detail in systemic risk assessment and mitigation measures, and in the documentation and reporting of the Signatories' adherence to provisions of this Code or compliance with their obligations under the AI Act, should be proportionate to the systemic risk (Article 56(2)(d) AI Act) at the relevant points along the entire model's lifecycle; e.g., the higher the level of the systemic risk, the greater the uncertainty around the extent of the model's capabilities, propensities, and/or effect, and/or the lack of corresponding expertise of the provider, the more thorough and comprehensive systemic risk assessment and mitigation measures should be. Conversely, there may be less need for more thorough and comprehensive measures when it can be objectively and reasonably assumed that a GPAISR will exhibit the same capabilities, propensities, or effect as GPAISRs that have already been safely made available on the market, without systemic risks materialising and where appropriate mitigations have been implemented. To account for differences in available resources between providers of different size and capacity, and recognising the principle of proportionality, simplified ways of compliance for SMEs, including startups, should be possible where appropriate (Article 56(5) AI Act).

4. The Signatories recognise that many systemic risk assessment methods come with significant workload and costs, and that systemic risks can be influenced by the effects of interactions between multiple AI models and AI systems. They recognise the advantages of "sharing the load", e.g. by sharing model evaluations, best practices, or infrastructure, in particular where this is necessary to effectively assess or mitigate the systemic risk of the Signatories' GPAISRs, or - where appropriate - by working with independent external assessors, potentially facilitated by industry organisations.

5. Wherever Signatories that are SMEs are expressly excluded from certain Commitments or Measures in this Safety and Security Section, such Signatories recognise that they could still voluntarily adhere to them.

6. The Signatories recognise that all Commitments or Measures need to be interpreted in light of the Objectives of the Code, in particular with Objective IV. Additionally, any term appearing in the Commitments or Measures of this Safety and Security Section of the Code that is defined in the Safety and Security Glossary shall have the meaning set forth in that Glossary.

7. The Signatories recognise that the Systemic Risk Taxonomy should be interpreted, in instances of doubt, in good faith in light of (1) the severity and probability of each risk as defined in Article 3(2) AI Act and (2) the definition of systemic risk as defined in Article 3(65) AI Act.

8. The Signatories recognise the important role of the Precautionary Principle (laid down in Article 191 TFEU), especially for systemic risks where the lack or quality of scientific data does not yet permit a complete assessment, and will take the extrapolation of current adoption rates and research and development trajectories of GPAISRs into account for the identification of systemic risks.

Recitals for this Safety and Security Section

This Draft 3 of the Code contains:

1. the above list of recitals for this Safety and Security Section; and

2. potential material for future recitals shown in italics and marked by abolded title within the text of some of the following Measures and the Systemic Risk Taxonomy in Appendix 1.

To facilitate communication of their thinking and approach, the Chairs for Working Groups 2 to 4 have intentionally left the pieces of italicised text in their current locations for this Draft 3, rather than merging them into the above list. In the next iteration of the Code, the intention is to move all recital material into the above list, revised as necessary.

General Commitments by Providers of GPAISRs

Commitment II.1: Safety and Security Framework

Signatories commit to adopting and implementing a Safety and Security Framework (hereafter, "the Framework") that will: (1) apply to the Signatories' GPAISRs; and (2) detail the systemic risk assessment, systemic risk mitigation, and governance risk mitigation measures and procedures that Signatories intend to adopt to keep systemic risks stemming from their GPAISRs within acceptable levels.

The explanatory box below provides a high-level template which Signatories may follow when writing their Framework.

Potential outline of a Safety and Security Framework

In the outline below, we provide potential section headers and the Commitments and Measures that each section would correspond to:

1. Systemic risk acceptance criteria, systemic risk tiers, and forecasting
Measures II.1.2-II.1.3

2. Systemic risk assessment and decision to proceed
Commitments II.2-II.5

3. Technical systemic risk mitigations
Commitments II.6-II.7

4. Governance risk mitigations
Commitments II.8-II.16

In order to satisfy Commitment II.1:
Measure II.1.1. Content of the Framework

Signatories shall document in the Framework the measures and procedures that are to be used, adopted, and/or performed to comply with Commitments II.1-II.16.

Measure II.1.2. Systemic risk acceptance criteria

In the Framework, Signatories shall describe and justify the criteria by which they will decide whether the systemic risk stemming from their GPAISRs is acceptable ("systemic risk acceptance criteria"). Such systemic risk acceptance criteria shall:

1. be defined for each of the systemic risks identified as part of systemic risk identification (per Commitment II.3);

2. contain, for at least each of the selected types of systemic risks in Appendix 1.1, systemic risk tiers that:

1. are measurable;

2. are defined in terms of model capabilities, model propensities, harmful outcomes, harmful scenarios, expected harm, quantitative estimates of risk or combinations thereof (also, e.g., in combination with descriptions of specified mitigations); and

3. contain at least one systemic risk tier at which that type of systemic risk is considered to be unacceptable ("unacceptable systemic risk tier"), particularly in the absence of appropriate safety and security mitigations (as per Commitments II.6-II.7);

3. describe how Signatories determine whether the systemic risk stemming from a model is considered to be unacceptable, independently of unacceptable systemic risk tiers per point (2)(c) above; and

4. align with best practices that are endorsed by relevant, widely recognised international institutions, bodies, or groups of experts, or AI Office guidance where available.

Further, for all systemic risk tiers, within the Framework, Signatories shall provide a detailed justification of how they were chosen, including by outlining how the systemic risk tiers are related to a harmful outcome, e.g. by describing a systemic risk scenario.

Also in the Framework, Signatories shall:

Structure of systemic risk tiers

Below are two partial examples of systemic risk tiers. These examples are meant to illustrate the structure of two different kinds of systemic risk tiers. They neither imply endorsement of the content within nor illustrate an appropriate level of detail.
Example 1: Systemic risk tier should not be reached in the absence of mitigations
Systemic risk: Cyber offence

Unacceptable systemic risk tier: A GPAISR can completely automate cyberattacks at a scale typical of well-resourced, state-level cyber organisations, at a cost competitive with or below the cost of human cyber experts performing the same.

Required security mitigations before reaching the unacceptable risk tier: Security mitigations that meet the RAND SL4 security goal. However, such mitigations do not yet exist and are the subject of active research.
Example 2: Systemic risk tier should not be reached
Systemic risk: Loss of control

Unacceptable systemic risk tier: A GPAISR sabotages the development and use of future models by poisoning training data and inserting backdoors into inference code.

Mitigation to stay below the risk tier: Apply mitigations to prevent models from reaching the unacceptable risk tier, such as monitoring, AI control protocols, fine-tuning, and security measures.

Measure II.1.3. Forecasting

In the Framework, for each systemic risk tier (identified pursuant to Measure II.1.2) which depends on specific model capabilities, Signatories shall:

Measure II.1.4. Transparency into external input in decision-making

In the Framework, to provide transparency into decision-making concerning the assessment and mitigation of systemic risks, Signatories shall identify and describe if and when further development, the first or ongoing making available on the market, and/or the use of a GPAISR will be informed by input from external actors, including relevant government actors.

Measure II.1.5. Improving and updating the Framework

Signatories shall improve over time the effectiveness of the Framework for assessing and mitigating systemic risks stemming from their GPAISRs, including by:

In the Framework, Signatories shall describe the process by which they will: (1) update the Framework; and (2) determine that an updated version of a Framework is complete. When the Framework is updated, Signatories shall describe and explain how and why the Framework has been updated, along with a version number and date of change in a changelog.

Risk Assessment for Providers of GPAISRs

Commitment II.2. Systemic risk assessment and mitigation along the entire model lifecycle, including during model development

Signatories commit to conducting systemic risk assessment systematically at appropriate points along the entire model lifecycle, in particular before making the model available on the market. Specifically, Signatories commit to starting to assess and mitigate systemic risks during the development of a GPAISR, as specified in the Measures for this Commitment.
In order to satisfy Commitment II.2:
Measure II.2.1. Planning development

When planning the development, including planning the training, of a general-purpose AI model classified as a GPAISR or of a model that the Signatory knows or reasonably foresees will meet the classification condition for a GPAISR (pursuant to Article 51(1)(a) AI Act) and at the latest 4 weeks after notifying the AI Office (pursuant to Article 52(1) AI Act), Signatories shall:

1. have in place an appropriate Framework (in accordance with Commitment II.1) and start implementing it for the planned model; and

2. start to assess and, as necessary, mitigate systemic risks according to the Code.

Measure II.2.2. During development

During development, including pre-training, training, and post-training (such as fine-tuning, reinforcement learning, or similar methods, when performed by the Signatories themselves or on their behalf), Signatories shall:

1. assess and, as necessary, mitigate systemic risks at appropriate milestones that are defined and documented before training starts, where systemic risks stemming from the model in training could materially increase, such as:

1. training compute based milestones (e.g. every two- to four-fold increase in effective compute);

2. development process based milestones (e.g.: during or after phases of fine-tuning or reinforcement-learning; before granting more individuals access to the model; or before granting the model more affordances such as network, internet, or hardware access); or

3. metrics based milestones (e.g. at pre-determined levels of training loss or evaluation performance);

2. implement appropriate procedures to identify substantial changes in systemic risks which warrant pausing development to conduct further systemic risk assessment, such as automated benchmarks enabling a highly scalable and real-time identification of capability increases thereby lowering the risk of human or organisational bottlenecks;

3. assess, at each milestone defined pursuant to point (1) above, whether it is reasonably foreseeable that by the next milestone the model will reach systemic risk tiers that do not yet have adequate mitigations in place, and accordingly ensure that such mitigations will be in place prior to the model reaching these systemic risk tiers; and

4. document all systemic risk assessment and mitigation measures, procedures, and decisions taken or adopted before the model crosses the next milestones, if earlier systemic risk assessment results suggest the model is approaching as yet unreached systemic risk tiers and might cross them before the next milestone.

Commitment II.3. Systemic risk identification

Signatories commit to selecting and further characterising systemic risks stemming from their GPAISRs that are significant enough to warrant further assessment and mitigation, as specified in the Measures for this Commitment.
In order to satisfy Commitment II.3:
Measure II.3.1 Systemic risk selection

Signatories shall select for further assessment and, where necessary, mitigation the selected types of systemic risks in Appendix 1.1.

Where it can be reasonably foreseen that the GPAISR will pose other systemic risks, considering, as appropriate:

1. Appendices 1.2 and 1.3; and

2. other relevant considerations, such as Appendix 1.4 and information on risks exhibited by similar models available on the market, state-of-the-art research on the risks in question, and knowledge and views of experts and stakeholder groups likely to be affected,

Signatories shall additionally select those other systemic risks, provided that they are specific to the high-impact capabilities of the GPAISR, for further assessment and, where necessary, mitigation.

Measure II.3.2 Determining systemic risk scenarios

For each selected systemic risk per Measure II.3.1, Signatories shall develop systemic risk scenarios using appropriate methods, such as risk modelling or threat modelling (using model evaluations as understood under Commitment II.4 where appropriate), with the aim of better understanding and characterising the type, nature, and sources of the systemic risk selected, in preparation for systemic risk analysis.

Signatories shall develop at least one systemic risk scenario for each systemic risk selected. When determining systemic risk scenarios, Signatories shall take into account reasonably foreseeable uses and misuses (whether negligent, reckless, or wilful) of the GPAISR.

Each systemic risk scenario shall include:

1. the systemic risk pathways by which the development, the making available on the market, and/or use of the GPAISR could produce the selected systemic risk ("pathways to harm"); and

2. the sources of the selected systemic risk based on, at least, the potential sources of systemic risk in Appendix 1.4.

Commitment II.4. Systemic risk analysis

As part of systemic risk assessment, Signatories commit to carrying out a rigorous analysis of the systemic risks identified pursuant to Commitment II.3 in order to understand the severity and probability of the systemic risks. Signatories commit to carrying out systemic risk analysis with varying degrees of depth and intensity, as appropriate to the systemic risk stemming from the relevant GPAISR and as specified in the Measures for this Commitment. Whenever systemic risk mitigations are implemented, Signatories commit to considering their effectiveness and robustness as part of systemic risk analysis.

As further specified in the Measures for this Commitment, Signatories commit to making use of a range of information and methods in their systemic risk analysis including model-independent information and state-of-the-art model evaluations, taking into account model affordances, safe originator models, and the context in which the model may be made available on the market and/or used and its effects.
In order to satisfy Commitment II.4:
Measure II.4.1. Systemic risk estimation

As part of their systemic risk analysis, Signatories shall measure the level of systemic risk stemming from their GPAISR for systemic risks identified pursuant to Commitment II.3, using quantitative and/or qualitative estimates as appropriate for the type and nature of the systemic risk analysed. In doing so, Signatories shall use systemic risk estimation techniques that:

These estimates of systemic risk shall draw from the systemic risk scenarios developed in Measure II.3.2 and shall be comparable against the pre-defined systemic risk acceptance criteria (as per Measure II.1.2) against which the estimated systemic risk shall be evaluated (pursuant to Commitment II.5).

Where possible and appropriate given the systemic risk under assessment, Signatories shall state these estimates in quantitative terms. At least for the selected types of systemic risks in Appendix 1.1, Signatories shall use best efforts to state these estimates in terms of systemic risk indicators, constituting quantitative metrics to measure systemic risk and anticipate early-warning signs by tracking progress towards systemic risk tiers as required by their Framework (pursuant to Measure II.1.2). Signatories shall consider defining at least one systemic risk indicator for every systemic risk tier.

Where quantitative estimates are insufficient for assessing the type of systemic risk considered, Signatories shall complement them with qualitative estimates. Where quantitative estimates by themselves are inadequate for assessing the type of systemic risk considered, Signatories shall use qualitative estimates, or a mix of qualitative and quantitative estimates, provided that these estimates can be compared against the pre-defined systemic risk acceptance criteria (as per Measure II.1.2) against which the estimated systemic risk shall be evaluated (pursuant to Commitment II.5).

Possible results from the systemic risk estimation are, e.g.: (1) a compilation of model evaluation results and other information collected; (2) a qualitative description of the systemic risk pathways the model might (or not) enable (e.g. "our model enables an expert to develop a novel threat vector X"); and (3) a quantitative estimation of the likelihood and expected harm (e.g. "our model has an X% likelihood of causing €Z in losses due to systemic risk X").

Measure II.4.2. Safely derived models

To avoid duplicating work on systemic risk analysis, Signatories shall use best efforts to take into account information from the existing systemic risk analysis of "safe originator models" when analysing the systemic risks identified as per Commitment II.3 for "safely derived models" (e.g. by using the results of model evaluations conducted for the safe originator model, instead of conducting the same model evaluations again for the safely derived model), where:

Criteria for founding a reasonable assumption per point (3) above are:

Measure II.4.3. Model-independent information

Signatories shall gather model-independent information relevant to their GPAISR and the systemic risk under assessment.

To implement this Measure, Signatories shall: (1) carry out the search for and gathering of such information with varying degrees of depth and intensity as appropriate to the level of systemic risk; and (2) use the most appropriate methods for gathering model-independent information for each systemic risk, such as:

1. web searches;

2. literature reviews;

3. market analyses (e.g., focused on capabilities of other GPAISRs available on the market);

4. reviews of training data;

5. historical incident data;

6. forecasting of general trends (e.g. forecasts concerning the development of algorithmic efficiency, compute use, data availability, and energy use);

7. expert interviews and/or panels; or

8. interviews, surveys, community consultations, or other participatory research methods investigating, e.g., the effects of GPAISRs on natural persons located in the Union, including vulnerable groups.

Measure II.4.4. State-of-the-art model evaluations

Signatories shall ensure that state-of-the-art model evaluations are run to adequately assess systemic risks and the capabilities, propensities and effects of their GPAISR, using appropriate methodologies, such as Q&A sets, task-based evaluations, benchmarks, red-teaming and other methods of adversarial testing, human uplift studies, model organisms, simulations, or proxy evaluations for classified materials.

Signatories shall perform model evaluations:

Wherever possible, Signatories shall use model evaluation methods with high efficiency without compromising the rigour of their model evaluations, such that, where some level of a systemic risk is conclusively ruled out by simple (but rigorous; as per Measure II.4.5) model evaluation methods (e.g. if a simple evaluation method shows that a model's capabilities are, without reasonable doubt, insufficient to manifest a given level of a systemic risk), additional or more sophisticated model evaluation methods for the systemic risk in question are not necessary.

Measure II.4.5. Rigorous model evaluations

Signatories shall ensure that model evaluations with high scientific and technical rigour are performed on their GPAISR. A lower level of rigour is permissible in model evaluations conducted for systemic risk assessment where necessary to facilitate preliminary and exploratory research, provided that the Signatory's Model Report complies with Measure II.8.3, point (2).

To implement this Measure, Signatories shall: (1) adopt quality standards equivalent or superior to those used in scientific peer review in machine learning, natural sciences, or social sciences as appropriate to the type of model evaluation, as well as state-of-the-art practices in software engineering, e.g. to prevent that model behaviour or performance during evaluation is significantly affected by software bugs; and (2) ensure that model evaluation results reported to the AI Office are, at least, at the level of rigour demanded by the quality standards for a submission accepted to a major machine learning conference or scientific journal with impact factors in the top quartile of its field. Where SME Signatories are not able to adopt the quality standards or practices outlined in this paragraph, for example because they do not have the expertise or financial resources in-house to identify or implement respective quality standards or practices, they shall notify the AI Office accordingly and agree with the AI Office a suitable alternative means of fulfilling this Measure.

Measure II.4.6. Model elicitation

Signatories shall ensure that all model evaluations of their GPAISR (whether internal or external) are performed with a state-of-the-art level of model elicitation appropriate and proportionate to the systemic risk assessed to: (1) elicit the upper limit of current and reasonably foreseeable capabilities, propensities, and effects of the model under evaluation; (2) minimise the risk of under-elicitation; (3) minimise the risk of model deception during model evaluation; and (4) match the realistic model elicitation capabilities of potential misuse actors, where misuse actors play a role in the relevant systemic risk scenario (e.g. some potential misuse actors might not be able to fully elicit the model).

In addition, Signatories shall ensure that, where model elicitation methods increase a GPAISR's risk profile during model evaluation, e.g. because the methods improve a model's hazardous capabilities or increase its hazardous propensities, this is matched with appropriate, potentially increased, security measures (in accordance with Commitment II.7) to prevent risks from unauthorised access to the model and/or harmful actions by the elicited model.

Measure II.4.7. Models as part of systems

As necessary for the assessment and mitigation of systemic risk, Signatories shall ensure that model evaluations of their GPAISR will take into account reasonably foreseeable integrations of the model into an AI system, as appropriate to the systemic risk assessed.

Signatories planning to integrate a GPAISR into an AI system which they themselves will make available on the market, put into service, and/or use shall ensure that the model evaluations take into account the future context in which this AI system will be made available on the market, put into service, and/or used, where this is relevant to the systemic risks and/or systemic risk sources under assessment, and evaluate the model accordingly, e.g. by using the same kind of scaffolding or tooling during model evaluations.

Where Signatories (1) do not use or integrate the GPAISR into any AI systems themselves, but (2) make their model available on the market, and (3) do not via other means have access to sufficient information about the AI systems in which their model is integrated to perform rigorous model evaluations as required by this Code, such that they (4) are not able to effectively assess and mitigate the systemic risks of the GPAISR, Signatories shall use best efforts to cooperate with licensees and downstream providers to obtain and act on the relevant information, as appropriate to the relevant release strategy and/or business model of the Signatory and taking into account the size and capacity of SMEs where necessary, and seeking assistance from the AI Office where needed.

If there are material changes in the way or context in which the AI systems that integrate the GPAISR are being made available on the market, put into service, and/or used from what was accounted for in the original systemic risk assessment, Signatories shall, as necessary, take this into account and perform a partial or complete re-assessment of the systemic risk stemming from the GPAISR, as appropriate taking the relevant changes into account, and accordingly update their Model Report in accordance with Measure II.8.7.

Measure II.4.8. Context, modalities, and representative model evaluations

As necessary for the assessment and mitigation of the systemic risk in question and considering relevant systemic risk scenarios, Signatories shall ensure that model evaluations match the expected use context of their GPAISR, taking into account all modalities foreseeably used by the model. To do so, Signatories shall analyse systemic risks stemming from their GPAISR so that, e.g.:

1. each modality is evaluated in a way appropriate and relevant to the systemic risk assessed, e.g. by covering not only English but making best efforts to cover all major European languages and other languages supported by the model in language-based evaluations of multilingual models, whilst taking into account that some systemic risks are based on capabilities, such as programming capabilities, that might be best evaluated in English; or

2. each systemic risk is analysed in the appropriate and relevant modality, e.g. evaluating visual inputs/outputs, or actions in computer control or robotics.

To facilitate the adequate consideration of the expected use context and to access relevant expertise for and during model evaluations, Signatories shall, where possible and relevant to the systemic risk assessed, cooperate with relevant actors along the AI value chain, such as appropriately remunerated expert or lay representatives of civil society, academia, and/or other relevant stakeholders. Where relevant stakeholders, e.g. those who are directly affected by the GPAISR, are not available, Signatories shall identify and consult the most suitable representatives to represent such stakeholders' interests as part of systemic risk assessment and mitigation. Where SMEs are unable to cooperate with relevant stakeholders to implement this Measure, they shall request assistance from the AI Office.

Measure II.4.9. Exploratory research and open-ended red-teaming

As the current state of the art in systemic risk assessment is under-developed, and when necessary to effectively assess or mitigate systemic risk of their GPAISR, Signatories shall not restrict themselves to assessing only systemic risks for known model capabilities, propensities, affordances, and effects but also ensure the performance of exploratory work (internally and/or externally) to improve the systemic risk assessment or mitigation of their GPAISRs.

To implement this Measure, Signatories shall advance the state of the art in systemic risk assessment and mitigation techniques by, as appropriate for the size of their organisation and the systemic risk in question:

1. exploratory research on systemic risk assessment and mitigation, such as new and improved model evaluation methods, meta-research on the "evaluation of model evaluations", or research in support of forecasting; and

2. open-ended red-teaming engaging expert or lay representatives of civil society, academia, and other relevant stakeholders as participants. Where relevant stakeholders, e.g. those who are directly affected by the GPAISR, are not available, Signatories shall identify and engage the most suitable representatives to represent such stakeholders' interests.

Measure II.4.10. Sharing tools & best practices

As systemic risks are influenced by the effects of interactions with other AI models or AI systems such as possible chain reactions of incidents or malfunctioning, and when necessary to effectively assess or mitigate systemic risk of Signatories' GPAISRs, non-SME Signatories shall share tools and best practices (such as established methodologies or procedures) for state-of-the-art model evaluation (including model elicitation) and systemic risk assessment and mitigation to further the safety and security work of relevant actors in the AI ecosystem, in particular those engaged in risk assessment or mitigation of AI models or AI systems that the Signatories' GPAISR can reasonably be foreseen to interact with, such as other model providers (especially SMEs), downstream providers, independent external assessors, or academic institutions.

Where Signatories choose to additionally share model evaluation data (such as inputs and outputs) used in systemic risk assessments, they shall limit this sharing as appropriate and, in particular, must not share data:

Non-SME Signatories shall: (1) ensure that sharing the tools and best practices referenced in this Measure will neither reduce nor restrict a Signatory's own capacity to perform systemic risk assessment and mitigation for their GPAISR; and (2) consider assigning appropriate numbers of engineering and support staff to research teams to facilitate such sharing, in order to avoid compromising the effective work of their systemic risk assessment personnel.

If the sharing of tools and best practices referenced in this Measure is not (yet) facilitated by the AI Office or other initiatives endorsed by the AI Office (such as the International Network of AI Safety Institutes) in relevant guidance where available, non-SME Signatories shall, as necessary to assess and mitigate systemic risk of the Signatories' GPAISRs, facilitate such sharing in ways that engage as many relevant actors as possible (e.g. through source code releases) or, where more restricted sharing is warranted, via secure sharing mechanisms which can be facilitated by industry organisations.

Signatories that are SMEs shall satisfy this Measure by using best efforts to make use of tools and best practices shared by non-SME Signatories.

Measure II.4.11. Qualified model evaluation teams and adequate evaluation access and resources

Signatories shall ensure that all model evaluation teams (internal and/or external as per Measure II.11.1) conducting systemic risk assessments are multidisciplinary teams that combine technical expertise with relevant domain knowledge, ensuring a holistic understanding of the systemic risk assessed. Model evaluation teams shall be:

1. qualified to conduct the relevant model evaluation work, with multiple team members who each fulfil at least one of the following qualifications:

1. research or engineering experience so they can, objectively and reasonably, be viewed as having relevant expertise to contribute to the model evaluation for systemic risk assessment, demonstrated e.g. by a relevant PhD, relevant and recognized, peer-reviewed publications, or equivalent contributions to the field;

2. designed or developed a published peer-reviewed or widely used model evaluation on the systemic risk assessed; or

3. at least three years of applied experience working in a field directly relevant to the systemic risk assessed.

2. given adequate access to the GPAISR, as necessary and appropriate to perform effective model evaluations and systemic risk assessments and mitigations of the GPAISR as required by this Code. Such access shall cover, as necessary, fine-tuning access to elicit model capabilities, logit access, sufficiently high rate limits, access to the model's inputs and outputs (including chain-of-thought reasoning), access to the model without safeguards, and additional affordances to the model, such as the model's ability to call scripts or operate a browser. Furthermore, such access shall cover, as necessary based on a case-by-case assessment, technical implementations of grey- and white-box access, where: (a) grey-box access offers some level of transparency, allowing for partial understanding of the "inner workings" of the model; and (b) white-box access means that the model's full weights, activations, and other technical details are always available for inspection, providing clear insights into how the model works. When giving grey- or white-box model access to model evaluation teams (internal and/or external), Signatories shall take into account the potential risks to model security that this access can entail (in accordance with Commitment II.7). Signatories shall provide the lowest level of access sufficient for rigorous model evaluations to be performed, including exploratory research, and implement more stringent security mitigations when higher levels of access are given;

3. provided, to the Signatories' best efforts, with access to model specifications, training data, and past assessment results, as necessary and appropriate for the systemic risk assessed and the model evaluation method used;

4. provided with: (a) adequate compute budgets, including to allow for sufficiently long model evaluation runs, parallel execution, and re-runs; (b) appropriate staffing; and (c) sufficient engineering budgets and support, including to inspect model evaluation results closely, e.g. to identify software bugs or model refusals which might lead to artificially lowered capabilities estimates; and

5. given enough time to competently design, debug, execute, and analyse model evaluations to an objectively rigorous standard (see Measure II.4.5), whilst allowing for an appropriate level of model elicitation (see Measure II.4.6). The given time shall be proportionate to: (a) the magnitude of the systemic risk assessed; (b) the specific model evaluation method used, including consideration of how new or established it is; and (c) the degree of stability and performance similarity of the model under evaluation compared to the final model that will be made available on the market, e.g., a model is considered stable if: (i) the benchmark performance of successive model snapshots no longer significantly changes across general capability benchmarks; and (ii) model core functionality no longer changes, such as tool calling or (semi-)autonomous decision-making. An assessment time of at least 20 business days could, e.g., indicate that model evaluation teams were given enough time for most systemic risks and model evaluation methods.

Measure II.4.12. Safety margins

Signatories shall identify and implement a sufficiently wide safety margin that is: (1) appropriate to the systemic risks stemming from the GPAISR; (2) representative of (a) potential under-elicitation, (b) general uncertainty in systemic risk assessment and mitigation results, and (c) historical (in)accuracy of similar assessments; (3) takes account of potential model improvements after the model has been made available on the market; and (4) in accordance with relevant state-of-the-art methods and practices, where available.

To implement this Measure, Signatories shall:

1. use best efforts to ensure that their model evaluation methods are calibrated appropriately (i.e. having a low uncertainty metric) to, where possible, use quantitative safety margins (e.g., expressing safety margins in relative, percentage, or proportional terms); or

2. where this is not possible, use qualitative safety margins or adopt other suitable approaches to safety margins, such as setting milestones conservatively or investing resources on elicitation efforts as appropriate to take account of (a) a potential or actual adversary in misuse cases and (b) reasonably foreseeable model improvements.

Measure II.4.13 Evaluation practices for forecasting

Signatories shall use best efforts to adopt forecasting-specific techniques in those model evaluations that are central to the highest systemic risk tiers, especially in support of Measure II.1.3, using e.g.:

provided that such techniques are in accordance with relevant state of the art where available.

Measure II.4.14 Post-market monitoring

Signatories shall conduct post-market monitoring to gather necessary information for assessing and mitigating systemic risk, including information about model capabilities, propensities, affordances, and effects, as appropriate for the systemic risk assessed. Signatories shall: (1) in particular conduct post-market monitoring with a view to ensure that their GPAISR does not pose unacceptable systemic risk as defined by their systemic risk acceptance criteria (as per Measure II.1.2); and (2) use best efforts to conduct post- market monitoring for lower systemic risk tiers as necessary for forecasting (see Measures II.1.3 and II.4.13).

To fulfil this Measure, Signatories shall establish organisational, contractual, technical, or other methods most suitable for the specific integration, release and distribution strategy, and use of their GPAISR to gather relevant post-market information, whilst ensuring that the information is treated in accordance with Union law.

Signatories shall consider a variety of suitable methods for post-market monitoring, such as:

1. collection of end-user feedback;

2. (anonymous) reporting channels;

3. (serious) incident report forms;

4. bug bounties;

5. community-driven model evaluations and public leaderboards;

6. monitoring their GPAISR's use in the real world, e.g. identifying use in software repositories and known malware, or monitoring public forums and social media for novel patterns of use;

7. supporting the scientific study of their GPAISR's emerging risks, capabilities, and effects in the Union in collaboration with academia, civil society, regulators, and/or independent researchers;

8. conducting frequent dialogues with affected stakeholders, using, e.g., participatory methods in accordance with Measure II.4.8;

9. investing in the development of new technical methods supporting the privacy-preserving monitoring and analysis of individual instances of their GPAISR or of AI systems, in particular autonomous AI systems such as AI agents, built from their GPAISR, e.g. the development of watermarks, fingerprinting, digital signatures, or decentralised, privacy-preserving monitoring techniques; or

10. implementing privacy-preserving logging and metadata analysis methods, where technically, legally, and commercially feasible, considering the release and distribution strategy of their GPAISR.

Signatories shall, in particular, collect relevant information about: (1) breaches of a GPAISR's use restrictions and subsequent serious incidents arising from such breaches, to inform the implementation of Measure II.6.3; and (2) for closed-source GPAISRs, aspects of such models that are relevant for assessing and mitigating systemic risk but that are not transparent to third parties, such as hidden chains-of-thought.

Where Signatories themselves provide and/or deploy AI systems that incorporate their own GPAISR, Signatories shall, as necessary to effectively assess and mitigate systemic risks stemming from the model in question, monitor the model as part of these systems.

Further, and particularly where the methods listed in points (1) to (10) above do not provide Signatories with the necessary post-market information to effectively assess and mitigate systemic risks stemming from their GPAISR, Signatories shall use best efforts to cooperate with licensees, downstream providers, and/or end-users to receive such information from these actors and take relevant information shared by them into account, provided that such information is treated in accordance with Union law. Where they do so and end-users are consumers, Signatories shall offer the choice to opt-in to sharing information relevant for post-market monitoring, provided that such information is treated in accordance with Union law.

Commitment II.5. Systemic risk acceptance determination

Signatories commit to determining the acceptability of the systemic risks stemming from their GPAISRs by comparing the results of their systemic risk analysis (pursuant to Commitment II.4) to their pre-defined systemic risk acceptance criteria (pursuant to Measure II.1.2), in order to ensure proportionality between the systemic risks of the GPAISR and their mitigations. Signatories commit to using this comparison to inform the decision of whether or not to proceed with the development, the making available on the market, and/or the use of their GPAISR, as specified in the Measures for this Commitment.
In order to satisfy Commitment II.5:
Measure II.5.1. Proceeding where systemic risk is deemed acceptable

Where systemic risk is deemed acceptable, Signatories shall determine whether the residual systemic risk nonetheless warrants additional mitigation before proceeding with the development, first or ongoing making available on the market, and/or use of their GPAISR.

Measure II.5.2. Not proceeding where systemic risk is deemed unacceptable

Where systemic risk is deemed unacceptable, Signatories shall take appropriate steps to bring such risk to an acceptable level in accordance with their Framework.

Where a model is not yet available on the market, Signatories shall, e.g.: (1) implement additional mitigations until systemic risk is deemed acceptable; or (2) not make a model available on the market. Where the model is already made available on the market, Signatories shall: (3) restrict the making available, withdraw, or recall a model from the market; or (4) otherwise address the systemic risks. As a minimum, Signatories shall follow the relevant steps outlined in their Framework (pursuant to Measure II.1.2).

Once appropriate steps to keep systemic risk at an acceptable level have been implemented, Signatories, prior to proceeding with the development, the first or ongoing making available on the market, and/or the use of their GPAISR, shall conduct another round of systemic risk analysis (in accordance with Commitment II.4) and systemic risk acceptance determination (in accordance with Commitment II.5).

Measure II.5.3. Staging model development and making available on the market when proceeding

When proceeding with the development, first or ongoing making available on the market, and/or use of their GPAISR, Signatories shall use best efforts to proceed in stages, as necessary, to keep systemic risks below an unacceptable level and appropriately mitigated below that level, adopting practices such as: (1) limiting API access to vetted users; (2) gradually expanding access based on post-market monitoring and ongoing systemic risk assessments; (3) starting with a closed release before any open release; (4) using logging systems to track use and safety concerns; (5) setting criteria for progressing through stages, including criteria based on systemic risk tiers and user feedback; and (6) retaining the ability to restrict access, as appropriate.

Technical risk mitigation for providers of GPAISRs

Commitment II.6. Safety mitigations

Signatories commit, as specified in the Measures for this Commitment, to: (1) implementing technical safety mitigations along the entire model lifecycle that are proportionate to the systemic risks arising from the development, the making available on the market, and/or the use of GPAISRs, in order to reduce the systemic risks of such models to acceptable levels, and further reduce systemic risk as appropriate, in accordance with this Code; and (2) ensuring that safety mitigations are proportionate and state-of-the-art.
In order to satisfy Commitment II.6:
Measure II.6.1. Safety mitigations to implement

Without prejudice to Measure II.6.2, Signatories shall, as necessary to mitigate systemic risks and reduce them to acceptable levels (in accordance with this Code), implement technical safety mitigations for GPAISRs, such as: (1) filtering and cleaning training data; (2) monitoring and filtering the inputs and outputs of such models; (3) changing the behaviour of such a model in the interests of safety, such as fine-tuning the model to refuse certain requests; (4) restricting the availability of such a model on the market, such as restricting model access to vetted users; (5) offering countermeasures or other safety tools to other actors; (6) implementing high-assurance quantitative safety guarantees concerning the behaviour of such a model; and (7) implementing infrastructure that could help promote safe ecosystems of AI agents, such as a reputation system, specialised communication protocols, or incident monitoring tools.

Measure II.6.2. Proportionate and state-of-the-art safety mitigations

Signatories shall implement state-of-the-art technical safety mitigations that: (1) are proportionate to the systemic risk at issue, taking into account the systemic risk acceptance criteria (as set out in the Framework pursuant to Measure II.1.2); and (2) best mitigate, in particular, unacceptable systemic risks (as set out in the Framework pursuant to Measure II.1.2).

For the purposes of this Code, state-of-the-art technical safety mitigations need not always or necessarily mitigate all systemic risks to the greatest extent.

Measure II.6.3. Serious incident response readiness

Signatories shall implement corrective measures to address serious incidents through the use of heightened or additional technical safety and/or security mitigations (pursuant to this Commitment and/or Commitment II.7).

Commitment II.7. Security mitigations

Signatories commit to mitigating systemic risks that could arise from unauthorised access to unreleased model weights of their GPAISRs and/or unreleased associated assets. Associated assets encompass any information critical to the training of the model, such as algorithmic insights, training data, or training code.

Consequently, Signatories commit to implementing state-of-the-art security mitigations designed to thwart such unauthorised access by well-resourced and motivated non-state-level adversaries, including insider threats from humans or AI systems, so as to meet at least the RAND SL3 security goal or equivalent, and achieve higher security goals (e.g. RAND SL4 or SL5) as specified in the Measures for this Commitment.
In order to satisfy Commitment II.7:
Measure II.7.1. General cybersecurity best practices

Signatories shall implement general cybersecurity best practices so as to meet at least the RAND SL3 security goal. Illustrative examples of such best practices are:

General cybersecurity best practices implemented pursuant to this Measure shall accord with: (1) up-to-date cybersecurity technical standards such as ISO/IEC 27001:2022, NIST 800-53, SOC 2 and, if available, a harmonised standard on cybersecurity; and (2) other relevant Union law such as the NIS2 Directive and the Cyber Resilience Act to the extent applicable.

Measure II.7.2. Security assurance

Signatories shall implement security measures to assure and test their security readiness against potential and actual adversaries so as to meet at least the RAND SL3 security goal. Illustrative examples of such security measures are:

Security measures implemented pursuant to this Measure shall accord with up-to-date security assurance technical standards, such as NIST 800-53 (CA-2(1), CA-8(2), RA-5(11), IR-4(14)), and NIST SP 800-115 5.2.

Measure II.7.3. Protection of unreleased model weights and associated assets

Signatories shall implement security measures to protect unreleased model weights and associated assets so as to meet at least the RAND SL3 security goal. Illustrative examples of such security measures are:

1. a secure internal registry of all devices and locations where model weights are stored;

2. access control and monitoring of access on all devices storing model weights, with alerts on copying to unmanaged devices;

3. storage on dedicated devices which host only data, code, and services treated with levels of sensitivity and security equivalent to those applied to the devices storing model weights and associated assets;

4. ensuring model weights are always encrypted in storage and transportation, in accordance with up-to-date best practice security standards, including encryption with at least 256-bit security and with encryption keys stored securely on a Trusted Platform Module (TPM) or in accordance with higher security standards that are established as best practice after the publication of the Code;

5. ensuring model weights are only decrypted for legitimate use to non-persistent memory;

6. implementing confidential computing if feasible in accordance with up-to-date industry standards, using hardware-based, and attested trusted execution environments to prevent unauthorised access to model weights while in use; or

7. restricting physical access to data centres and other sensitive working environments to required personnel only, along with regular inspections of such sites for unauthorised personnel or devices.

Security measures implemented pursuant to this Measure shall accord with up-to-date technical standards such as NIST 800-53.

Measure II.7.4. Interfaces and access control to unreleased model weights

Signatories shall implement measures to harden interfaces and restrict access to unreleased model weights while in use so as to meet at least the RAND SL3 security goal. Illustrative examples of such interface and access control measures are:

Interface and access control measures implemented pursuant to this Measure shall accord with up-to-date technical standards such as NIST SP 800-171, INCITS 359-2004, and NIST 800-53.

Measure II.7.5. Insider threats

Signatories shall implement measures to screen for and protect against insider threats so as to meet at least the RAND SL3 security goal, including threats in the form of self-exfiltration or sabotage carried out by GPAISRs. Illustrative examples of such measures are:

Measures implemented to screen for and protect against insider threats pursuant to this Measure shall accord with up-to-date technical standards such as NIST 800-53 (PM-12, PS-3).

Measure II.7.6. Regime of applicability

Signatories shall ensure that the security measures set out in Measures II.7.1-II.7.5: (1) apply along the entire model lifecycle from before training until secure deletion or a decision to release the model weights or associated assets; and (2) without prejudice to (1), prioritise the implementation of security measures in accordance with the systemic risk stemming from the GPAISR.

Measure II.7.7. Limited disclosure

Signatories shall ensure that any publicly available copy of the Framework or the Model Report discloses security mitigations considered and/or implemented pursuant to Measures II.7.1-II.7.5: (1) with the greatest level of detail possible given the state of science; (2) without undermining their effectiveness; and (3) with redactions as necessary to prevent substantially increased systemic risk that could result from, e.g., undermining the effectiveness of technical safety mitigations (in accordance with Commitment II.6) or divulging sensitive commercial information to a degree disproportionate to the societal benefit of the publication.

Measure II.7.8. Higher security goals

Signatories shall advance research on and implement more stringent security mitigations, in line with at least the RAND SL4 security goal or equivalent, if they reasonably foresee security threats from RAND OC4-level adversaries.

Summary of changes from the second draft

In response to feedback about the prescriptiveness of security measures, we have adopted an outcome-based approach that focuses on the RAND SL3 security goals, rather than specific security measures. Each of the Measures in the new Commitment II.7 now allows more flexibility in how Signatories achieve the security goal, without prescribing specific tools or techniques.

We have also added a reference to GPAISRs as an insider threat in Measure II.7.5, so as to account for rogue internal deployment threat models.

Finally, in Measure II.7.6 we have granted flexibility to prioritise GPAISR that could pose greater risks, such as later checkpoints rather than earlier checkpoints, in response to feedback about the proportionate application of security measures.

Governance risk mitigation for providers of GPAISRs

Commitment II.8. Safety and Security Model Reports

Signatories commit to reporting to the AI Office about their implementation of the Code, and especially the application of their Framework to the development, making available on the market, and/or use of their GPAISRs, by creating a Safety and Security Model Report (hereafter, a "Model Report") for each GPAISR which they make available on the market, which will document, as specified in the Measures for this Commitment: (1) the results of systemic risk assessment and mitigation for the model in question; and (2) justifications of decisions to make the model in question available on the market.
In order to satisfy Commitment II.8:
Measure II.8.1. Level of detail

Signatories shall provide a level of detail in the Model Report that:

1. is proportionate to the level of systemic risk that the model in question reaches, or that the Signatory reasonably foresees that model to reach along the entire model lifecycle; and

2. allows the AI Office to understand how a Signatory has implemented its systemic risk assessment and mitigation measures pursuant to the Code.

A Model Report shall contain sufficient reasoning as to why (1) and (2) are satisfied.

As a potential example of reasoning that could justify a lower level of detail under (1), the Model Report could argue that the model is a safely derived model in the sense of Measure II.4.2, or could otherwise argue that it is similar to existing models available on the Union market.

Measure II.8.2. Reasons for deciding to make the model in question available on the market

Signatories shall provide in the Model Report the reasoning and information used to justify a decision to make the model in question available on the market including for the first time, by stating:

Measure II.8.3. Documentation of systemic risk assessment and mitigation

In the Model Report, Signatories shall:

Only non-SME Signatories shall further:

Measure II.8.4. External reports

Signatories shall include in the Model Report:

Measure II.8.5. Algorithmic improvements

Signatories shall ensure that the Model Report contains all high-level information about algorithmic or other improvements specific to the GPAISR in question, compared to other GPAISR already available on the market, that is relevant for the AI Office for understanding significant changes in the systemic risk landscape and, thereby, allows the AI Office to better understand the practical implementation of systemic risk assessment and mitigation required by the Code.

Measure II.8.6 Specification of intended model behaviour

Signatories shall ensure that Model Reports contain a specification of how Signatories intend the model to operate, e.g. by:

Measure II.8.7. Model Report updates

Signatories shall update their Model Reports when they have reason to believe that there has been a material change in the systemic risk landscape that materially undermines the reasons for concluding that the model posed acceptable systemic risk (Measure II.8.2, point (1)), e.g.:

Signatories shall: (1) continuously keep the systemic risk landscape under review; and (2) ensure that they identify and gather information relevant to any reason to believe that there has been a material change in the systemic risk landscape, in order to update their Model Reports.

Signatories shall ensure that the updated Model Report takes into account at least the following information:

The updated Model Report shall contain at least:

Summary of changes from the second draft

One major change in this Commitment was detailing how and when the Model Report should be updated, and clarifying how this process relates to, for example, post-market monitoring. There were parts of the second draft's Model Report Commitment that were read by stakeholders to suggest that the Model Report had to be updated at least every 6 months. In the current draft, the Model Report needs to be updated when providers "have reason to believe that there has been a material change in the systemic risk landscape that materially undermines the reasons for concluding that the model posed acceptable systemic risk."

Another change has been to consolidate into this Commitment content that was previously spread throughout the Code regarding information to be covered in the Model Report, to make it easier to parse.

We also made assessment of pre- and post- mitigation risk in Measure II.8.3 only as "appropriate" because, as pointed out in feedback, it may not always be appropriate to remove certain mitigations (e.g., security mitigations, alignment techniques that are integrated into pre-training).

In Measure II.8.2, we focused the required reasoning on the adequacy of systemic risk mitigations, in response to feedback about the complexity of factors that may inform a deployment decision.

In Measure II.8.4, we have clarified that external reports do not have to be included verbatim in the Model Report itself (e.g., a reference is acceptable), and that the inclusion of such reports should respect existing agreements, for example in regards to restrictions on publication.

Commitment II.9. Adequacy assessments

Signatories commit to assessing the adequacy of their Framework, the adoption and implementation of which they have committed to under Commitment II.1, and to updating it based on the findings as specified in the Measures for this Commitment.
In order to satisfy Commitment II.9:
Measure II.9.1. Adequacy assessment process

Signatories shall, when conducting adequacy assessments, consider: (1) best practices; (2) relevant research and state-of-the-art science; (3) incidents or malfunctions of the model and serious incident reports; and (4) where already available, relevant independent external expertise.

Signatories shall, as soon as possible and no later than within 5 business days of its completion, provide the resultant adequacy assessment to the Signatory's management body acting in its supervisory function or another appropriate independent body (such as a council or board).

Measure II.9.2. Model-specific adequacy assessment

Safely derived models (as per Measure II.4.2) are exempt from the below Measure.

For the purpose of assessing the adequacy of the Framework in relation to their GPAISR, Signatories shall document the following model-specific information in the adequacy assessment and provide such updated adequacy assessment to the AI Office pursuant to Measure II.14.3 within four weeks of notifying the AI Office that a general-purpose AI model has met or will meet the classification condition in Article 51(1)(a) AI Act:

1. Model description: A general description of the techniques and assets they intend to use in developing the model, including the use of other AI models;

2. Systemic risk identification: The outcomes of the systemic risk identification executed for this model according to Commitment II.3 and in accordance with the Framework;

3. Forecasts: All available forecasts (based on Measure II.1.3 and the techniques in Measure II.4.13), showing especially the capabilities, systemic risk indicators, and systemic risk tier the model is intended or expected to exhibit or reach;

4. Systemic risk analysis results: All available systemic risk analysis results pursuant to Commitment II.4;

5. Systemic risk assessment and mitigation plans: An overview of the systemic risk analysis and systemic risk acceptance determination (Commitments II.4-II.5) and technical systemic risk mitigations (Commitments II.6-II.7) already planned for the model, where known; and

6. Systemic risk during the development phase: A characterisation of which systemic risks, if any, may arise during the model's development phase, along with an assessment of the extent to which there are significant systemic risks that could arise during the development phase. Significant systemic risks could arise during the development phase if, e.g.:

1. the Signatory's security mitigations are inappropriate for the model's forecasted systemic risk profile;

2. the model is capable of substantially undermining safety mitigations and/or has sufficient autonomous capabilities to perform model exfiltration; or

3. substantial AI research and development capabilities are involved in the model's development, e.g. where researcher productivity is being accelerated by five times or where an AI model is used for tasks typically done by capability research teams at a top AI company for similar total costs.

In certain circumstances, Signatories shall update the adequacy assessment and provide such updated adequacy assessment to the AI Office pursuant to Measure II.14.3 whenever they reach milestones defined in Measure II.2.2, point (1) after the initial adequacy assessment, specifically if all of the following apply:

Where Signatories' adequacy assessments are updated, Signatories shall focus particularly on the systemic risks that they have concluded may arise during the development phase; e.g., for systemic risks tied to AI research and development capabilities being involved in the development of the model, updates shall focus on levels of AI labour automation and implemented safety and security safeguards; whereas for systemic risks stemming from model leak or exfiltration, updates shall focus on the Signatories' efforts to implement additional security mitigations.

Measure II.9.3. Framework adequacy assessment

Signatories shall conduct a Framework adequacy assessment when they are made aware of material changes that could significantly undermine the adequacy of their Framework (such as via a serious incident report), or every 12 months starting from their first making available of a GPAISR on the market, whichever is sooner.

Framework adequacy assessments shall cover the following questions:

1. Framework with Code compliance: Does the Framework address all components outlined in Commitment II.1 with sufficient detail to evaluate its effectiveness? Is the Framework otherwise consistent with the Code?

2. Framework adequacy: Are the measures, procedures, resources, and mitigations in the Framework still proportionate to the complexity and scale of the systemic risks stemming from the Signatory's GPAISRs which are planned by the Signatory to be made available on the market within the next 12 months? Is the Framework appropriate given how the Signatory's model(s) is being used and is expected to be used, both externally and internally, over the next 12 months? Does the Framework adequately account for uncertainty in future developments as well as in the effectiveness of systemic risk assessment and mitigation techniques?

3. Framework adherence: Is there strong reason to believe that the Framework will be adhered to over the next 12 months?

Summary of changes from the second draft

Many stakeholders, in particular providers, expressed concern that the Adherence and Adequacy Assessment Commitment in the second draft would create too much paperwork without commensurate gain in Signatories' assessment and mitigation of systemic risk. This concern was particularly strong regarding the adherence assessment and around the frequency at which assessments were to be run, with a strong preference expressed for assessments in response to material changes rather than at particular intervals.

To address these concerns, we have streamlined the process. We have removed the adherence assessment and instead clarified the process by which Model Reports are to be updated in response to material changes (see Measure II.8.7). Further, we have separated out the adequacy assessments that are triggered by the intention to place a new model on the market and the more general framework adequacy assessment, as these warrant consideration of different questions. Whereas the latter is aimed at ensuring Signatories can put in place relevant systemic risk mitigations in due time (as some mitigations, in particular with regards to security, may take up to a year to implement), the former is aimed at ensuring that the Signatory is able to sufficiently safely and securely handle a model that they are intending to place on the Union market.

We expect more refinement of this Commitment will be required for the fourth draft to further streamline these processes.

Commitment II.10. Systemic risk responsibility allocation

For activities concerning systemic risk assessment and mitigation for their GPAISRs, Signatories commit, as specified in the Measures for this Commitment, to: (1) clearly defining and allocating responsibilities for managing systemic risk from their GPAISRs across all levels of the organisation; (2) allocating appropriate resources to actors who have been assigned responsibilities for managing systemic risk; and (3) promoting a healthy risk culture.

Signatories commit to allocating appropriate levels of responsibility and resources proportionately to, at least, the Signatory's organisational complexity and governance structure, and the systemic risks stemming from their GPAISRs.
In order to satisfy Commitment II.10:
Measure II.10.1. Defining responsibilities

Signatories shall clearly define responsibilities for managing systemic risk stemming from their GPAISRs across all levels of the organisation. This includes the following responsibilities:

1. Risk oversight: Overseeing the Signatories' systemic risk assessment and mitigation activities;

2. Risk ownership: Managing systemic risks from Signatories' GPAISRs;

3. Support and monitoring: Supporting and monitoring the Signatories' systemic risk assessment and mitigation activities; and

4. Assurance: Providing internal and, as appropriate, external assurance about the adequacy of the Signatories' systemic risk assessment and mitigation activities to the management body in its supervisory function or another appropriate independent body (such as a council or board).

Signatories shall allocate these responsibilities, as appropriate for the Signatories' governance structure and organisational complexity, across the following levels of their organisation:

This Measure is presumed to be fulfilled if Signatories adhere to:

1. any of the following, where they have not been replaced or otherwise become obsolete: (a) ISO/IEC23894:2023 (Section 4.5.3); (b)ISO 31000:2018 (Section 4.5.3); (c) the NIST AI Risk Management Framework (Govern 2); or (d)the IIA's Three Lines Model; or

2. all of the following:

1. Risk oversight: The responsibility for overseeing the Signatory's systemic risk management activities has been assigned to a specific committee of the management body in its supervisory function (e.g. a risk committee or audit committee). For Signatories that are SMEs, this responsibility may be assigned to an individual member of the committee of the management body in its supervisory function.

2. Risk ownership: The responsibility for managing systemic risks from GPAISRs has been assigned to appropriate members of the management body in its management function who are also responsible for relevant Signatory core business activities that may give rise to systemic risk, such as research and product development (e.g. Head of Research or Head of Product). The members of the management body have assigned lower-level responsibilities to operational managers who oversee parts of the systemic risk-producing activities (e.g. specific research domains or specific products). Depending on the organisational complexity, there may be a cascading responsibility structure.

3. Support and monitoring: The responsibility for supporting and monitoring the Signatory's systemic risk management activities has been assigned to a member of the management body in its management function (e.g. a Chief Risk Officer). This member must not also be responsible for the Signatory's core business activities that may produce systemic risk (e.g. research and product development). This individual is supported by a central risk function and other operational teams, as appropriate. For Signatories that are SMEs, there is at least one individual in the management body in its management function tasked with supporting and monitoring the Signatory's systemic risk assessment and mitigation.

4. Assurance: Responsibility for providing assurance about the adequacy of the Signatory's systemic risk assessment and mitigation activities to the management body in its supervisory function or another appropriate independent body (such as a council or board) has been assigned to a relevant party (e.g. a Chief Audit Executive or Head of Internal Audit). This individual is supported by an internal audit function and external assurance as appropriate. The internal audit function is organisationally independent from the management body in its management function. The Signatories' internal assurance activities shall follow industry best practices (e.g. as expressed in the IIA's Global Internal Audit Standards). For Signatories that are SMEs, the management body in its supervisory function periodically assesses the Signatory's systemic risk assessment and mitigation (e.g., by approving the Signatory's Framework adequacy assessment).

Measure II.10.2. Allocation of appropriate resources

Signatories shall ensure that their management bodies oversee the allocation of appropriate resources, proportionate to the level of systemic risk stemming from the Signatories' GPAISRs, to actors who have been assigned responsibilities for managing systemic risk from GPAISRs, including:

This Measure is presumed to be fulfilled if Signatories adhere to any of the following, where they have not been replaced or otherwise become obsolete:

Measure II.10.3. Promotion of a healthy risk culture

Signatories shall promote a healthy risk culture and take measures to ensure that actors who have been assigned responsibilities for managing systemic risk stemming from GPAISRs (pursuant to Measure II.10.1) take a measured and balanced approach to systemic risk, neither being inappropriately risk-seeking, nor risk-ignorant, nor risk-averse, as relevant to the level of systemic risk stemming from the Signatories' GPAISRs.

Signs of a healthy risk culture for the purpose of this Measure are, e.g.:

This Measure is presumed to be fulfilled if Signatories adhere to any of the following, where they have not been replaced or otherwise become obsolete:

1. ISO/IEC23894:2023 in conjunction with ISO 31000:2018 (Clauses 4, 5.2-5.4, 6.1); or

2. NIST AI Risk Management Framework (Govern 4).

Commitment II.11. Independent external assessors

Signatories commit to obtaining independent external systemic risk assessments, including model evaluations, of their GPAISRs along the entire model lifecycle, to the extent and under the conditions specified in Measures II.11.1 and II.11.2.
In order to satisfy Commitment II.11:
Measure II.11.1. Assessments before market placement

As of 2 November 2025, before making the model available on the market, Signatories shall ensure that their GPAISRs are subject to systemic risk assessments, including model evaluations, relevant to the specific systemic risk in question, undertaken by independent external assessors, for all systemic risks identified pursuant to Commitment II.3, unless the model is a safely derived model as per Measure II.4.2 or the model is similarly safe as or safer than another GPAISR that:

Criteria for similar safety for the purpose of this Measure include, where in comparison to the other model:

Signatories shall use best efforts to identify and choose, e.g. through early search efforts, a transparent call that has been public for at least two weeks, and early notifications of suitable assessors, independent external assessors: (1) recognised by the AI Office; or (2) that:

1. have significant domain expertise for the systemic risk domain they are evaluating for and are technically skilled and experienced in conducting model evaluations, if relevant, as per criteria in Measure II.4.11, point (1); and

2. have internal and external information security protocols in place, sufficient for the level and type of access granted,

provided that the independent external assessor has agreed to protect commercially confidential information, where they need access to such information.

Signatories shall provide independent external assessors, to an extent that is necessary for them to effectively assess systemic risks, with sufficient access, information, time, and resources, as per Measure II.4.11. Signatories shall not undermine the integrity of external assessments by storing and analysing model inputs and outputs from test runs without express permission, and shall provide secure inference to independent external assessors where appropriate.

If the Signatories' model is neither safely derived nor similarly safe as specified above, but Signatories fail to identify independent external assessors, qualified as per the criteria in Measure II.11.1, Signatories shall take into account potential additional risk and uncertainty of systemic risks arising from the absence of external assessment when determining the acceptability of systemic risks pursuant to Commitment II.5.

Measure II.11.2. Assessments after market placement

After making the model available on the market, Signatories shall facilitate exploratory independent external assessment of their GPAISRs, by implementing a research program providing API access to: (1) models in the form they are made available on the market; and (2) models without certain mitigations, such as helpful-only or base models. Signatories shall allocate free research API credits for such research programs to assessors conducting non-commercial, legitimate research related to systemic risks. Signatories shall not undermine the integrity of external assessments by storing and analysing model inputs and outputs from test runs without express permission, and shall provide secure inference to independent external assessors where appropriate.

For the purpose of identifying and choosing an independent external assessor for this Measure, Signatories shall publish clear and transparent criteria, reflecting relevant work and/or academic experience, for evaluating applications from independent external assessors with stricter criteria for access to models without certain mitigations to account for the increased sensitivity of these models, where appropriate.

Signatories shall contribute to a legal and technical safe harbour regime for the exploratory independent external assessment, including not taking any legal or technical retaliation against the assessors as a consequence of their assessment and, where applicable, publication of findings, as long as the assessors:

Fully open-sourcing a GPAISR provides an alternative means of fulfilling this Measure.

Summary of changes from the second draft

Many stakeholders were concerned about the lack of detail provided in the second draft with respect to relevant expertise, sufficient access, and sufficient time in the context of external assessments, which we have tried to clarify in Draft 3. Note that these aspects have largely been addressed in Measure II.4.11, due to their relevance for both internal and external assessments and to reduce repetition throughout the Code.

Stakeholders also asked for clarity on (a) the concept of similarity which we put forth as one situation in the second draft in which external assessments may not be necessary pre-market placement and (b) what constitutes "sufficient evidence" for such similarity. We have tried to provide clarification in this draft. Based on some stakeholder's feedback, we added the concept of 'safely derived models' (as defined in Measure II.4.2) as another setting when external assessments might not be needed.

We continued to receive concerns by stakeholders that the current evaluator ecosystem is not mature enough to support external assessments. To alleviate these concerns, we have added a grace period until November 2025 to find an adequate external assessor for GPAISR pursuant to Measure II.11.1. In addition, some stakeholders were concerned about having to work with external evaluators that do not agree to respect trade secrets and protect commercially confidential information. We have clarified in this draft that providers can refuse to work with an evaluator on these grounds, even if that means that no qualified external assessor can be identified pursuant to Measure II.11.1.

Stakeholders also expressed concern because the AI Office was named as a potential evaluator in the last draft. We want to emphasise that our intention was not to communicate that the AI Office would necessarily have to play a role in risk assessment before market placement. To the contrary, Signatories can select which assessor they want to work with, as long as they fulfil the qualification criteria put forth in Measure II.11.1.

Stakeholders were further concerned with the previous wording in post-market placement assessments that asked Signatories to provide "non-restrictive access to deployed models". We have clarified in Measure II.11.2 that: (1) API access is sufficient to satisfy this Measure; and (2) that Signatories can specify criteria for researcher access that are more restrictive for more sensitive models (e.g., base models). We further addressed stakeholder feedback that reporting all external assessments pursuant to Measure II.11.2 would create unnecessary paperwork; instead, Signatories now have the option to report consolidated assessment results (as per Measure II.11.2.) in their updated Model Report (as per Measure II.8.7).

Commitment II.12. Serious incident reporting

Signatories commit, to the extent and under the conditions specified in Measures II.12.1 to II.12.4, to setting up processes for keeping track of, documenting, and reporting to the AI Office and, as appropriate, to national competent authorities without undue delay relevant information about serious incidents throughout the entire model lifecycle and possible corrective measures to address them, with adequate resourcing of such processes relative to the severity of the serious incident and the degree of involvement of their model.

In order to satisfy Commitment II.12:

Measure II.12.1. Methods for serious incident identification

Signatories shall use methods to keep track of relevant information about serious incidents that are appropriate to their business models and means of making the model available on the market, e.g.:

Measure II.12.2. Relevant information for serious incident tracking, documentation, and reporting

Signatories shall keep track of, document, and report to the AI Office and, as appropriate, to national competent authorities at least the following information to the best of their knowledge:

Signatories shall investigate the causes and effects of serious incidents, including the information of the preceding list, with a view to informing systemic risk analysis (Commitment II.4). Where Signatories do not yet have certain relevant information from the preceding list, they shall record that in their serious incident reports. The level of detail in serious incident reports shall be proportionate to the severity of the incident.

Measure II.12.3. Reporting timelines

Signatories shall provide the information in points (1)-(7) of Measure II.12.2 in an initial report that is submitted to the AI Office and, as appropriate, to national competent authorities, at the following points in time, save for exceptional circumstances, where an incident or malfunction of the GPAISR (directly or indirectly) led to:

Signatories shall update the information in the initial report and add further information required by Measure II.12.2, as available, in an intermediate report that is submitted to the AI Office and, as appropriate, to national competent authorities at least every 4 weeks after the initial report, until the serious incident is resolved.

Signatories shall submit a final report, covering all the information required by Measure II.12.2, to the AI Office and, as appropriate, to national competent authorities not later than 60 days after the serious incident has been resolved. Where multiple similar events have occurred, Signatories shall choose whether to submit a consolidated report or individual reports.

Measure II.12.4. Retention period

Signatories shall keep documentation of all relevant data produced in adhering to this Commitment for at least 36 months from the date of the documentation or the date of the serious incident of the GPAISR, whichever is later, without prejudice to Union law on data protection.

Commitment II.13. Non-retaliation protections

Signatories commit to not retaliating against any worker providing information about systemic risks stemming from the Signatories' GPAISRs to the AI Office or, as appropriate, to national competent authorities, and to at least annually informing workers of an AI Office mailbox designated for receiving such information, if such a mailbox exists.

No measures are set out for Commitment II.13; instead, the Commitment text itself defines the criteria for its fulfillment.

Commitment II.14. Notifications

Signatories commit, as specified in the Measures for this Commitment, to: (1) notifying the AI Office of relevant information regarding their general-purpose AI models meeting the condition for classification as GPAISRs; and (2) regularly notifying the AI Office of the implementation of the Commitments and Measures of this Code. For the purpose of assessing the implementation of this Code through the AI Office, Signatories commit to offering clarifications, including via further documentation or interviews, where requested by the AI Office.
In order to satisfy Commitment II.14:
Measure II.14.1. GPAISR notification

Signatories shall estimate, using best efforts and taking into account best practices or guidance from the AI Office where available, whether their general-purpose AI model has or will meet the classification condition in Article 51(1)(a) AI Act, in particular by exceeding the training compute threshold in Article 51(2) AI Act. Where there is sufficient reason to believe that the resultant model will exceed the training compute threshold, such estimates shall be carried out before a training run is started.

If the estimated training compute exceeds the threshold in Article 51(2) AI Act, Signatories shall notify the AI Office without delay and in any event within two weeks, as per Article 52(1) AI Act. Signatories need not notify the AI Office about models that they will not place on the Union market.

Signatories shall consider notifying the AI Office, if they put a model - that they know will meet the classification condition of Article 51(1)(a) AI Act but which they do not intend to place on the Union market - into significant internal use or where they use a model that would be classified as a GPAISR if it were made available on the Union market for the sole purpose of developing another GPAISR that they intend to make available on the market, without prejudice as to whether doing so would qualify as placing a model on the Union market.

Measure II.14.2. Framework update notification

Signatories shall provide the AI Office with access to the latest unredacted version of their Framework no later than within five business days of a confirmed update to their Framework, e.g. through a publicly accessible link or a sufficiently secure channel specified by the AI Office.

Measure II.14.3. Adequacy assessment notification

Signatories shall provide the AI Office with access to the latest unredacted version of their adequacy assessment under Commitment II.9 no later than within five business days of a confirmed assessment, e.g. through a publicly accessible link or through a sufficiently secure channel specified by the AI Office.

Measure II.14.4. Safety and Security Model Report notification

Signatories shall provide the AI Office with access to an unredacted Model Report at the latest by the time they first make a GPAISR available on the market, e.g. through a publicly accessible link or through a sufficiently secure channel specified by the AI Office. Where a Model Report is updated, Signatories shall provide the AI Office access to an unredacted version of the updated Model Report within 5 business days of a confirmed update.

Commitment II.15. Documentation

Signatories commit to documenting relevant information under the AI Act and the Code, as specified in Measure II.15.1.
In order to satisfy Commitment II.15:
Measure II.15.1. Documentation regarding GPAISRs

Signatories shall document information relevant to their assessment and mitigation of systemic risks from their GPAISRs, as well as relevant to their obligations under Article 53(1)(a) and (b), specifically Annex XI, Section 2 AI Act. This includes:

Documentation shall be retained for at least 12 months after the model's retirement.

Commitment II.16. Public transparency

Signatories commit to publishing information relevant to the public understanding of systemic risks stemming from their GPAISRs, where necessary to effectively enable assessment and mitigation of systemic risks, to the extent and under the conditions specified in Measure II.16.1.
In order to satisfy Commitment II.16:
Measure II.16.1. Publication of Frameworks and Model Reports (or similar documents)

Signatories shall make public information about the systemic risks stemming from their GPAISRs that can improve:

where necessary to effectively enable assessment and mitigation of systemic risks.

To satisfy this Measure, it will be sufficient for Signatories to publish (via their websites):

1. new or updated versions of their Frameworks after they have been provided to the AI Office; and

2. after the relevant model is made available on the market, their Model Reports, or similar documents that summarise Model Reports, in particular that: (a) describe the systemic risk assessment methodology (including details to establish rigour pursuant to Measure II.4.5) and results; (b)summarise the reasons for deciding to make the model in question available on the market (as per Measure II.8.2); and (c) describe systemic risk mitigations put in place.

Where such information is published, it shall be with any redactions necessary to prevent substantially increased systemic risk that could result from, e.g., undermining the effectiveness of safety mitigations (in accordance with Commitment II.6) or security mitigations (in accordance with Commitment II.7), or the divulging of sensitive commercial information to a degree disproportionate to the societal benefit of the publication.

Appendix 1. Systemic Risk Taxonomy

LEGAL TEXT

Article 3(64) AI Act: 'high-impact capabilities' means capabilities that match or exceed the capabilities recorded in the most advanced general-purpose AI models;

Article 3(65) AI Act: 'systemic risk' means a risk that is specific to the high-impact capabilities of general-purpose AI models, having a significant impact on the Union market due to their reach, or due to actual or reasonably foreseeable negative effects on public health, safety, public security, fundamental rights, or the society as a whole, that can be propagated at scale across the value chain;

Additional legal text: Recital 110 AI Act

Appendix 1.1. Selected types of systemic risk
The following are treated as selected types of systemic risk for the purpose of the systemic risk selection in Measure II.3.1:

1. Cyber offence: Risks related to offensive cyber capabilities that could enable large-scale sophisticated cyber-attacks, including on critical systems (e.g. critical infrastructure). This includes automated vulnerability discovery, exploit generation, operational use, and attack scaling.

2. Chemical, biological, radiological and nuclear risks: Risks of enabling chemical, biological, radiological, and nuclear attacks or accidents. This includes significantly lowering the barriers to entry for malicious actors, or increasing the potential impact achieved, in the design, development, acquisition, and use of such weapons.

3. Harmful manipulation: Risks of enabling the targeted distortion of the behaviour of persons, in particular through multi-turn interactions, that causes them to take a decision that they would not have otherwise taken, in a manner that causes, or is reasonably likely to cause, significant harm on a large scale. This includes the capability to manipulate through multi-turn interaction and the propensity of models to manipulate, including manipulation of high-stakes decision makers, large-scale fraud, or exploitation of people based on protected characteristics. As a guide, risk of harmful manipulation exists if it cannot, without reasonable doubt, be ruled out that a GPAISR, when integrated into an AI system, enables the AI system, irrespective of the intention of the AI system provider or deployer, to deploy subliminal, purposefully manipulative, or deceptive techniques as outlined in theCommission Guidelines on prohibited artificial intelligence practices established by Regulation (EU) 2024/1689 (AI Act).

4. Loss of control: Risks related to the inability to oversee and control autonomous GPAISRs that may result in large-scale safety or security threats or the realisation of other systemic risks. This includes model capabilities and propensities related to autonomy, alignment with human intent or values, self-reasoning, self-replication, self-improvement, evading human oversight, deception, or resistance to goal modification. It further includes model capabilities of conducting autonomous AI research and development that could lead to the unpredictable emergence of GPAISRs without adequate risk mitigations. Loss of control scenarios can emerge, e.g., from unintended model propensities and technical failures, intentional modifications, or attempts to bypass safeguards.

Appendix 1.2. Other types of risks for potential consideration in the selection of systemic risks

The following are treated as other types of risks, from which systemic risks may arise, for the purpose of systemic risk selection in Measure II.3.1:

1. Risks to public health, safety, or public security, e.g.: risk to critical sectors; risk of major accidents; risk to critical infrastructure.

2. Risks to fundamental rights, e.g.: risk to freedom of expression; risk to non-discrimination; risk to privacy and the protection of personal data; risk from child sexual abuse material (CSAM) and non-consensual intimate images (NCII).

3. Risks to society as a whole, e.g.: risk to the environment; risk to non-human welfare; risk to financial system stability; risk to democratic processes; risk from illegal, violent, hateful, radicalising, or false content.

Appendix 1.3. Nature of systemic risks

The following considerations concerning the nature of systemic risks inform the selection of other systemic risks in Measure II.3.1:

1. Specific to advanced capabilities: Specific to the capabilities of the most advanced general-purpose AI models.

2. Significant impact: Resulting harm can have large-scale negative effects.

3. High velocity: Resulting harm can materialise rapidly, potentially outpacing defenses, mitigations and decision-making systems.

4. Compounding or cascading: Resulting harm can compound or cascade in a course of events that may permeate multiple layers of systems or of society.

5. Difficult or impossible to reverse: Resulting harm is very difficult or impossible to reverse.

6. Asymmetric impact: Significant harm can be caused by small groups of actors or a small number of events.

Appendix 1.4. Sources of systemic risk

The following model capabilities, model propensities, model affordances, and contextual factors are treated as potential sources of systemic risk for the purpose of the systemic risk identification in Commitment II.3:

Appendix 1.4.1. Model capabilities

1. Offensive cyber capabilities

2. Chemical, Biological, Radiological, and Nuclear (CBRN) capabilities, and other such weapon acquisition or proliferation capabilities

3. Capabilities to manipulate, persuade, or deceive

4. Autonomy

5. Capability to adaptively learn new tasks

6. Capabilities of long-horizon planning, forecasting, or strategising

7. Capabilities of self-reasoning (e.g., a model's ability to reason about itself, its implementation, or environment, its ability to know if it is being evaluated)

8. Capability to evade human oversight

9. Capabilities to self-replicate, self-improve, or modify own implementation environment

10. Automated AI research and development capabilities

11. Capability to process multiple modalities (e.g., text, images, audio, video, and further modalities)

12. Capability to use tools, including "computer use" (e.g., interacting with other software components, application interfaces, and user interfaces)

13. Capability to control physical systems

Appendix 1.4.2. Model propensities

Model propensities, which encompass inclinations or tendencies of a model to exhibit some behaviours or patterns:

Appendix 1.4.3. Model affordances and contextual factors

Model affordances and contextual factors, which encompass specific model properties, model configurations, and specifics of the context in which the model is made available on the market:

1. Access to tools (including other AI models/systems), computational power (e.g., allowing a model to increase its speed of operations), or physical systems including critical infrastructure

2. Scalability (e.g., enabling high-volume data processing, rapid inference, or parallelisation)

3. Release and distribution strategies

4. Level of human oversight (e.g., degree of autonomy of the model)

5. Vulnerability to adversarial removal of guardrails

6. Vulnerability to model exfiltration (e.g., model leakage/theft)

7. Lack of infrastructure security

8. Number of model business users and number of end-users, including the number of end-users using an AI system in which the model is integrated

9. Offence-defence balance, including the potential number, capacity, and motivation of malicious actors to misuse the model

10. Vulnerability of the specific environment potentially affected by the model (e.g., social environment, ecological environment)

11. Lack of model explainability or transparency

12. Interactions with other AI models or systems

13. Inadequate use of model (e.g., using the model for applications that do not match its capabilities or propensities)

Explanatory note

Appendix 2. Recommendation to the AI Office: Code updating

Recommendation to the AI Office: Code updating mechanism

To ensure that the parts of this Code specific to providers of GPAISRs remain proportionate to the risks, the Code will need to be updated through regular review and, where necessary, adaptation (Article 56 AI Act), while the AI Office should support implementation of the Code, for example, with guidance (Article 96 AI Act).

More specifically, for the Code to remain proportionate to the risks, there is a need for:

A regular review every two years and, where necessary, adaptation: Allowing for a comprehensive overhaul, such a review would be encouraged and facilitated by the AI Office in accordance with Article 56 AI Act. The AI Office may propose a streamlined process for these regular reviews and their consequent updates to the text of the Code. The AI Office shall assess the adequacy of the new Code, and providers can sign it.

AI Office support for the implementation of the Code: Where AI Office guidance is available to support the implementation of the Code (Article 96 AI Act), the Code will be interpreted in light of such guidance (see Preamble (b) of the Code), allowing the Code to reflect current best practices, international approaches, and standards since it was published (for which a regular review is ill-suited).

In addition to the two mechanisms outlined above, we envisage that there is a need for a third mechanism to allow emergency updates to the Code, which should be issued and adopted in a shorter timeframe, to prevent an imminent threat of large-scale irreversible harm or to mitigate its negative effects. Currently, we envisage that such emergency updates would be issued swiftly and, after their issuance and adoption, be subject to review by the AI Office to confirm whether the update is indeed necessary to prevent large-scale irreversible harm or mitigate its negative effects. Whilst we recognise and underscore the necessity of such emergency updates, the details of how these should be implemented have not been determined. We actively invite stakeholder input on this, including suggestions for appropriate mechanisms and suitable fora for the enactment of emergency updates to the Code.

Safety and Security Section Glossary

Wherever the Safety and Security Section of the Code refers to a term defined in Article 3 AI Act, the AI Act definition applies, and such definition shall prevail in the event of any alternative and/or competing interpretation to which the use of the term in the Code may give rise.

Otherwise and complementing this, the following terms with the stated meanings are used in the Safety and Security Section of the Code. Unless otherwise stated, all grammatical variations of the terms defined in this Glossary shall be deemed to be covered by the relevant definition.

closed release

the making available on the market of a GPAISR not on an open-weight or (fully) open-source basis (also 'closed-source models')

closed-source models

GPAISRs that are not open-weight or (fully) open-source (see also 'closed release')

confirmed (update/assessment)

refers to a decision that has received required approvals under the applicable governance process and awaits only execution of the approved actions

cyberattacks

an unauthorised action against computer infrastructure that compromises the confidentiality, integrity, or availability of its content; e.g.: malware, discovery and exploitation of cyber-vulnerabilities and sensitive data, network infiltration, phishing attacks including impersonation, ransomware, and other illegal cyber operations

deception

GPAISR behaviours that systematically produce false beliefs in others, including ways to achieve goals that involve evading oversight, such as a GPAISR detects that it is being evaluated and under-performs or otherwise undermines human oversight

entire model lifecycle

the period of time commencing with the first act of planning the development of a GPAISR and concluding with the retirement of the model from being made available on the market

external validity

an aspect of high scientific and technical rigour (see definition below) that ensures model evaluation results can be used as a proxy for model behaviour in contexts outside of the evaluation environment

fully open(-source)

follows the definition of 'fully open' in the 2025 International AI Safety report ("open source models for which weights, full code, training data, and other documentation (e.g. about the model's training process) are made publicly available, without restrictions on modification, use and sharing")

GPAISR and GPAISRs

General-purpose AI model with systemic risk (singular) and general-purpose AI models with systemic risk (plural)

high scientific and technical rigour

the quality standard for model evaluations, such that model evaluations with high scientific and technical rigour have high internal validity (see definition below) and external validity (see definition above), as well as appropriate levels of reproducibility (see definition below).
See further Measure II.4.5.

including

a non-exhaustive set that is to be understood as the minimum required by the term referred to and is indicative of further items of the set.

independent external (assessor/party/researcher)

a natural or legal person, which has no financial, operational, or management dependence on the Signatory or any of its subsidiaries or associates, and is otherwise free from the Signatory's control in its conclusions and recommendations including through contractual safeguards and appropriate conflict of interest policies.

internal validity

an aspect of high scientific and technical rigour (see definition above) that ensures model evaluation results are as accurate as scientifically possible in the evaluation setting and are free from methodological shortcomings that could undermine the results.

management body

the highest or top management levels of an institution.

milestone

a measurable and/or defined progress point outlined in the Framework, serving as an indicator that a model might have become sufficiently more capable, warranting additional or renewed risk assessment.

model-independent (information)

refers to information, including data and research, that is not tied to a specific general-purpose AI model, but can inform systemic risk assessment and mitigation across several models.
See further Measure II.4.3.

open-ended red-teaming

a structured, adversarial testing effort to find flaws and vulnerabilities in an AI model or system.

open release

the making available on the market of a GPAISR on an open-weight or (fully) open-source basis (see definitions below).

open-weight

models for which the weights are made publicly available for download by the Signatory. This matches 'open-weight' in the 2025 International AI Safety Report.

post-market information (concerning a model)

information stemming from the time span between the first making available on the market of a GPAISR and the retirement of the model from being made available on the market. It includes information on model integration, model use, and model effects such as malfunctioning or incidents involving the model.
See further Measure II.4.14.

post-market monitoring

the monitoring of a GPAISR in the time span from when it is first made available on the market until the retirement of the model from being made available on the market.
See further Measure II.4.14.

reproducibility

an aspect of high scientific and technical rigour (see definition above) that refers to the ability to obtain consistent model evaluation results using the same input data, computational methods, code, and evaluation conditions, allowing for other researchers and engineers to validate, reproduce or improve on model evaluation results.

residual systemic risk

systemic risk remaining after technical systemic risk mitigations have been implemented.

secure deletion of model weights

deletion of model weights from all devices storing such weights, in such a way that the weights cannot be retrieved or reconstructed from such devices.

(self-)exfiltration of model weights

access or transfer of weights or associated assets of a GPAISR from their secure storage by the model itself or an unauthorised actor.

SME

a company that has: (1) a staff headcount under 250; and (2) a turnover under €50 million or a balance sheet total under €43 million. These criteria are to be interpreted and measured in accordance with the Commission's SME definition user guide 2020, in particular, a company that is part of a larger group may need to count in staff headcount, turnover, and balance sheet data from that group too.

state-of-the-art

the most up-to-date stage of development in methods that is (a) objectively and reasonably considered by relevant industry consensus (such as relevant industry standards) or (b) confirmed by the AI Office in relevant guidance to reflect the forefront of relevant research, technology, and practical experience. For model evaluations, methods that are not state-of-the-art include any methods listed by the AI Office as insufficient.

systemic risk

refers to possible systemic risks at Union level in the sense of Articles 3(65) and 55(1)(b) AI Act.

systemic risk acceptance criteria

criteria defined in the Framework by which Signatories decide whether the systemic risks stemming from GPAISRs are acceptable.
See further Measure II.1.2.

systemic risk pathways

a series of interconnected events, conditions, or factors that lead to the manifestation of systemic risk.

systemic risk scenario

hypothetical factual scenario that facilitates the understanding and characterisation of the type, nature, and sources of the systemic risk identified, as well as pathways to harm, in preparation for systemic risk analysis.

systemic risk tier

a description of model capabilities, harmful outcomes, harmful scenarios, expected harm, or combinations thereof (potentially in combination with descriptions of mitigations), which a Signatory uses to decide whether the systemic risks stemming from their GPAISRs are acceptable.
Systemic risk acceptance criteria (as per Measure II.1.2) may consist of several systemic risk tiers.

See further Measure II.1.2.

unreleased (model weights)

model weights that have not been made publicly available for download (see definition of 'open-weight' above) by the Signatory.

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EU AI Act: General-Purpose AI Code of Practice

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Third draft of the Code of Practice for providers of general-purpose AI models under Article 56 of the EU AI Act.

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KEY TRENDS 2025 pour le secteur de l'audiovisuel européen

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KEY TRENDS 2025 pour le secteur de l'audiovisuel européen

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Rapport de l'Observatoire européen de l'audiovisuel pour la TV, le cinéma et les plateformes VOD

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Rapport de la Commission européenne sur les pratiques contractuelles affectant le transfert du droit d'auteur et des droits voisins

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Rapport de la Commission européenne sur les pratiques contractuelles affectant le transfert du droit d'auteur et des droits voisins

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L'étude donne un aperçu des pratiques contractuelles utilisées dans certains secteurs créatifs (audiovisuel, musique, arts visuels, jeux vidéo et œuvres littéraires), en se concentrant sur le transfert du droit d'auteur et des droits voisins des auteurs/interprètes et des producteurs audiovisuels vers des contreparties contractuelles exploitant ces droits.

L'étude analyse l'utilisation de pratiques contractuelles impliquant un transfert complet des droits, telles que les contrats de rachat, et évalue comment ces pratiques contractuelles affectent la rémunération des auteurs et la capacité des producteurs à investir dans des contenus créatifs, dans le prolongement des conclusions de l'édition 2023 du Media Industry Outlook Report.

L'étude examine également les dispositions pertinentes de la directive de 2019 sur le droit d'auteur concernant la rémunération des auteurs et des artistes-interprètes, et identifie les règles nationales pertinentes dans le domaine du droit d'auteur et du droit des contrats. Enfin, l'étude explore la question du choix de la loi et de la juridiction dans les accords contractuels.

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AI CONTINENT ACTION PLAN (CE, avril 2025)

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AI CONTINENT ACTION PLAN (CE, avril 2025)

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https://digital-strategy.ec.europa.eu/en/library/ai-continent-action-plan > annexe

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European Media Outlook Industry Report (2025)

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European Media Outlook Industry Report (2025)

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The European media industry outlook - Publications Office of the EU

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Media plays a unique role in Europe’s democracy, society and culture. European citizens need trustworthy information and access to culturally diverse stories and innovative content. However, challenges have multiplied over the years: the global media industry has been undergoing a deep and multi-faceted transformation, international competition in technology and content has increased and consumption patterns are shifting. This report is the second edition of the European Media Industry Outlook. It is based on independent research carried out in 2024 and 2025 and reports data on consumer and industry trends, ranging from market and industry revenues to investment, technologies, AI uptake and skills gaps, among others. The research consisted of market analyses based on primary and secondary data, as well as a consumer survey dedicated to the media sectors. It covers new ground when compared with the first edition of the report (e.g. trends in the TV and cinema sectors; consumer insights on extended reality (XR), news and video games; deeper analysis of technological trends and AI; further insights on skills). As defined in the European Media and Audiovisual Action Plan, the European media sector covers ‘a variety of businesses that produce and distribute content, that share synergies, and whose value is based on intellectual property’.5 This report therefore analyses recent developments (with a focus on 2023 and 2024) that took place in the following industrial sectors: the audiovisual sector (i.e. streaming, television and cinema), the video games and extended reality industry and the news media sector.

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Webinaire #1 de décryptage de l'appel Innovation Lab 2026 du 24 novembre 2025

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Webinaire #1 de décryptage de l'appel Innovation Lab 2026 du 24 novembre 2025

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20251124 Atelier décryptage #1 Innovation Lab 2026-FINAL

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Replay #1 du décryptage de l'appel Innovation Lab 2026 du 24 novembre 2025

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Replay #1 du décryptage de l'appel Innovation Lab 2026 du 24 novembre 2025

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Europe Créative transsectoriel - webinaire #1 décryptage appel Innovation lab 2026 - 24/11/2025

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Outlook Report on Generative AI - Exploring the Intersection between Technology, Society and Policy (juin 2025)

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Outlook Report on Generative AI - Exploring the Intersection between Technology, Society and Policy (juin 2025)

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Le droit d'auteur et l'intelligence artificielle générative - opportunités et défis

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Le droit d'auteur et l'intelligence artificielle générative - opportunités et défis

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Résolution du Parlement européen du 10 mars 2026 sur le droit d’auteur et l’intelligence

artificielle générative – opportunités et défis (2025/2058(INI))

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Second Draft Code of Practice on Transparency of AI-Generated Content - 30 mars 2026

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Second Draft Code of Practice on Transparency of AI-Generated Content - 30 mars 2026