Control A.6.2.8 – AI System Recording of Event Logs

In todays article by Kimova AI, we explore Control A.6.2.8 – AI System Recording of Event Logs, a critical control within ISO/IEC 42001 that focuses on maintaining traceability, accountability, and transparency through effective logging of AI system events.

Event logs provide the evidence needed to understand how an AI system behaves in real-world conditions. Without proper logging, organizations lose visibility into system actions, decision pathways, errors, and potential misuse—making governance, audits, and incident investigations extremely difficult.

What This Control Requires

Control A.6.2.8 requires organizations to ensure that AI systems record relevant events throughout their operation in a way that supports monitoring, audits, investigations, and continuous improvement.

Event logging should capture information such as:

System start, stop, and restart events
Model execution and decision events
Input and output records (where appropriate and lawful)
Configuration changes and model updates
Access and authentication activities
Errors, anomalies, and system failures
Human interventions or overrides
Security-related events
Performance and threshold breaches

Logs must be sufficient to reconstruct events, understand AI behaviour, and support accountability.

Why AI Event Logging Matters

AI systems operate at scale and speed. When something goes wrong, organizations must be able to answer questions such as:

What decision did the AI system make?
When did it happen?
What data influenced the decision?
Was human intervention involved?
Did the system behave as intended?
Was there any security or ethical concern?

Without proper logs, these questions cannot be reliably answered.

Effective event logging supports:

Audit and compliance requirements under ISO 42001 and related standards
Incident investigation and root-cause analysis
Detection of bias, drift, or abnormal behaviour
Security monitoring and breach detection
Operational oversight and performance monitoring
Legal defensibility and accountability

Key Logging Principles Under ISO 42001

To meet Control A.6.2.8, organizations should follow these principles:

Relevance

Log only what is necessary to support governance, compliance, and operational needs.

Accuracy and Integrity

Logs must be accurate, tamper-resistant, and protected from unauthorized modification.

Privacy and Data Protection

Ensure logs comply with data protection laws (e.g., GDPR), especially when personal data is involved.

Retention and Availability

Define retention periods based on legal, regulatory, and business requirements, and ensure logs are retrievable when needed.

Correlation and Analysis

Logs should be structured to allow correlation across systems, models, and events.

Implementation Guidance

Organizations can implement this control effectively by:

Defining a logging policy specific to AI systems
Identifying mandatory AI events that must be logged
Integrating logs with SIEM and monitoring tools
Implementing access controls and integrity protection for logs
Establishing regular log reviews and alerts
Aligning AI logs with incident response and risk management processes
Documenting logging configurations and responsibilities

At Kimova AI, we strongly emphasize that event logs are not just technical records—they are essential governance artifacts that demonstrate responsible AI management.

Conclusion

Control A.6.2.8 ensures that AI systems operate with traceability, accountability, and transparency. Proper event logging enables organizations to detect issues early, respond effectively, and demonstrate compliance with ISO 42001 and other regulatory frameworks. By embedding robust logging practices into AI system design and operations, organizations can build trust with stakeholders and uphold the principles of responsible AI.

In tomorrow’s article by Kimova.AI, we’ll explore Annex A Control A.7.1 – Data for AI Systems, where we’ll explore how organizations can ensure that data used for AI is appropriate, high-quality, well-governed, and compliant, forming a reliable foundation for trustworthy AI outcomes.

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