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Challanges encountered in handling Big Data

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Challanges encountered in handling Big Data

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Data Volume - too much data

Data Processing Speed - high speed is needed to be processed

Data Variations problems - Too many types and formats of data to be analyzed

Data Inconsistencies and Redundancies - Data may have no value or repeated

by the nerds,

Rafiqi

Afiq

Muhammad

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1. Data Integrity and Accuracy

IoT devices generate vast amounts of data, but if sensors malfunction or data is tampered with, the accuracy of audit findings may be compromised.

There may be no clear audit trail for validating sensor-generated data.

2. Security Risks

IoT devices are often vulnerable to cyberattacks.

If compromised, they could provide false data or be used to access internal systems, affecting audit reliability.

3. Data Overload

IoT systems generate huge volumes of data (Big Data), which can overwhelm traditional audit tools and processes.

Filtering relevant data for audit purposes becomes complex.

4. Standardization Issues

Lack of industry-wide standards for IoT data formats and protocols can make it hard to integrate and analyze data from different devices during audits.

5. Privacy and Compliance

IoT may capture sensitive or personal data, raising compliance concerns (e.g., GDPR, HIPAA).

Auditors must ensure the data collected meets legal and ethical guidelines.

6. Integration with Existing Audit Systems

Traditional audit systems may not be compatible with IoT-generated data, requiring new tools, training, or infrastructure.

7. Cost and Complexity

Implementing and maintaining IoT-based auditing systems can be expensive.

Requires auditors with both IT and domain expertise.

8. Reliability of Devices

IoT devices may fail or provide inconsistent data due to hardware/software limitations or environmental factors.

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Data integrity and reliability

(generate large volumes of data)

Cybersecurity and Fraud Risks

(exposure to cyber threats)

Lack of Physical Evidence

(transactions and operations are digital with no physical trail, authenticity of transactions)

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1 ) Confidentiality - sensitive data of client can be exposed

2) Data integrity - Alter, delete, Corrupt with unauthorised access

3) Distruption to audit - fieldwork or analyse the issue

4) Unable to acces to system- restricted to access

5) Communication breakdown - email etc can be hijacked

by - moctar , husni, fadhli, irfan

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1. Ransomware Attacks

files are locked, and ransom is demanded

2. Cloud Security Misconfiguration

sensitive data expose to public/hackers

3. AI-Driven Cyber Attacks

Social engineering becomes more believable and dangerous

4. IoT Insecurity

IoT devices can be hijacked easily due to weak security

Iskandar

Dinnie

Maoxinyu

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Challenges in Using CAATs:

1. Lack of technical skills - Auditors may lack the necessary IT skills to fully utilize CAATs

2. Security and confidentiality - Auditors must handle sensitive client data, and using CAATs increases the risk of data breaches

3. Compatibility issues - may arise if the client’s data formats, don’t integrate smoothly with CAATs.

4. Lack of standardization - There is no universal standard for how CAATs should be implemented.

5. Overreliance on technology - relying solely on CAATs may cause auditors to miss contextual or judgement-based issues.

by syahmi, firdaus, amir, ikmal

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1. non-compliance with standards

- using pirated software violates it governance and legal compliance

-this can lead to failed audit and legal consequences for the organization

2. resistance from staff and management

- auditors may face pushback when trying to remove the pirated software especially when they think it as essential software

- management may also be unaware to address due to cost concerns

3. security risks

- pirated software often contains malware and backdoor (cyber security threats)

- IT auditors must assess whether these pose ongoing to data integrity or confidentiality

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1.Lack of Direct Access to Systems

Auditors can't physically inspect servers or access infrastructure since it's managed by cloud providers (e.g., AWS, Azure).

This limits the ability to perform independent verification of controls (e.g., firewalls, backup systems, configurations).

2. Shared Responsibility Confusion

In cloud models, responsibilities are split between the provider and the client.

If auditors don’t clearly define which controls belong to whom, important areas may go unaudited.

For example, the provider manages physical security, but the client handles user access controls — both need auditing.

3. Data Location Uncertainty

Cloud services may store data across multiple countries without the user knowing.

This makes it difficult to verify compliance with laws like GDPR, HIPAA, or PDPA (Malaysia).

Auditors must assess whether data is being stored or processed in legally acceptable locations.

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Model Reliability and Accuracy

must be highly accurate to support professional judgements

can misguide the auditors and leading to audit failure.

Security and Confidentiality

Cloud based AI tools could raise concerns about data breaches or unauthorized access

Exposure of client financial data or proprietary information

Ethical and Bias Concerns:

Bias Detection: Auditing fairness metrics and mitigation strategies.

Ethical Alignment: Verifying alignment with organizational values and societal norms.