# data[].attributes.body
Information security protects all of the different types of data, while cyber security focuses only on digital data. Both aim to safeguard sensitive information from unauthorized access.

Organizations can combat emerging threats such as ransomware, cloud vulnerabilities, IoT risks, zero trust, and AI attacks with proactive cybersecurity and pervasive security culture.

Organizations balance security by implementing role-based access, user-friendly solutions, training, monitoring, and collaboration while staying agile to adapt to evolving threats and business needs.

Text resource: https://online.champlain.edu/blog/information-security-vs-cybersecurity#:~:text=While%20there%20is%20some%20overlap,but%20also%20in%20other%20contexts.

Video resource:

https://youtu.be/inWWhr5tnEA?si=RXMYQPrbzNB_TKcc

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-Organizations can leverage artificial intelligence (AI) and machine learning (ML) technologies in several ways:

1. Anomaly Detection: AI/ML algorithms can analyze vast amounts of data to detect anomalies or unusual patterns that may indicate a security breach.

2. Threat Intelligence: AI can process and analyze threat intelligence feeds from various sources to identify emerging threats and vulnerabilities.

3. Automated Response: AI-powered systems can automate incident response processes by identifying and mitigating security threats in real-time.

-The use of AI in cybersecurity also raises several ethical implications, including:

1. Bias: AI algorithms may inherit biases present in the data used for training. Biased algorithms can lead to discriminatory outcomes, such as unfairly targeting certain individuals or groups based on demographic factors.

2. Privacy Concerns: Organizations must ensure that AI technologies adhere to relevant regulations (e.g., GDPR) and implement robust security measures to safeguard sensitive information.

3. Transparency and Accountability: AI algorithms are often perceived as "black boxes" due to their complex decision-making processes. Lack of transparency can make it difficult to understand how AI systems reach conclusions.

Text Resource:https://www.elastic.co/explore/security-without-limits/elastic-ai-assistant-analyst-report?utm_campaign=Google-NB-Security-AMER-NA&utm_content=Security-AI-B&utm_source=google&utm_medium=cpc&device=c&utm_term=ai%20tools%20for%20cybersecurity&gad_source=1&gclid=Cj0KCQjwn7mwBhCiARIsAGoxjaKYfFLVwXvd3_JrmkL_Lb7br5Ll0oUFbQXPnQwz-S4YUczlUw8HKGsaArhqEALw_wcB

Video Resource:https://www.youtube.com/watch?v=lO2Cf5BmjvI

# data[].attributes.body
Text:

https://ovic.vic.gov.au/privacy/resources-for-organisations/biometrics-and-privacy-issues-and-challenges/

Video:

https://youtu.be/CMv1fsi2Qj4?si=oXAjg3jpOk_YMM6v

Having multiple ways of authentication like biometrics and strong passwords is important for authentication

Also having user education in order to teach people that having strong passwords is important and using MFA across all platforms so there is no weak link in the system.

The problems with MFA would be the data privacy in keeping people's profiles and data safe, another issue  is getting people's consent in order to have their bio data collected and how it will be protected and used.