Control A.6.2.6 – AI System Operation and Monitoring

In today’s article by Kimova AI, we explore Control A.6.2.6 – AI System Operation and Monitoring, a crucial requirement within ISO/IEC 42001 that ensures organizations maintain oversight, stability, and responsible performance of AI systems after deployment.

Once an AI model goes live, the real work begins. Unlike traditional software, AI systems evolve through real-world interactions, data drift, and environmental changes. Without structured operational controls and continuous monitoring, even a well-designed AI system can quickly become inaccurate, biased, or non-compliant.

What This Control Means

Control A.6.2.6 requires organizations to establish processes to operate, monitor, and maintain AI systems in a controlled, safe, and accountable manner.

This includes:

Ensuring the AI system performs as intended during real-world usage
Tracking model behaviour, outputs, and any deviations
Monitoring for ethical concerns, bias, or harmful outcomes
Detecting performance degradation and initiating corrective actions
Maintaining system logs for traceability, audits, and investigations
Ensuring human oversight remains active and clearly defined

The goal is to ensure the AI system continues to meet accuracy, safety, compliance, and fairness requirements throughout its lifecycle.

Why AI Operation & Monitoring Matters

AI models face natural degradation over time due to:

Data drift (input data changes)
Concept drift (relationship between variables changes)
Model fatigue due to aging or misalignment
User behaviour shifts
Environmental or business changes

Without strong monitoring, organizations may unknowingly operate AI systems that:

produce incorrect or unsafe decisions
trigger automated actions that cause harm
violate regulatory requirements
create discrimination or bias
damage customer trust

Therefore, ISO 42001 enforces structured monitoring to keep AI systems reliable and responsible.

Key Practices for ISO 42001 Compliance

To comply with this control, organizations should implement:

Operational Procedures

Define how the AI system is started, stopped, updated, maintained, and overseen by responsible personnel.

Continuous Performance Monitoring

Set up metrics such as:

accuracy
false positives / false negatives
latency
model drift indicators
bias metrics
stability and error rates

Logging & Alerting

Maintain logs for inputs, outputs, model decisions, failures, and user feedback
Create alerts for anomalies or threshold breaches

Human Oversight Mechanisms

Clear responsibilities for who monitors, who approves changes, and how interventions are performed.
Incident Handling for AI Systems

Documented processes to handle AI-related incidents such as:
unexpected outputs
ethical concerns
security breaches
safety impacts

Regular Reviews & Re-validation

Periodic checks to confirm AI systems still align with intended use, risk appetite, and organizational policies.

Conclusion

Control A.6.2.6 emphasizes that AI governance doesn’t end at deployment — it evolves with ongoing supervision. By operationalizing and monitoring AI systems properly, organizations ensure their solutions remain safe, accurate, ethical, and compliant over time.

In tomorrow’s article by Kimova.AI, we’ll explore Annex A Control A.6.2.7 – AI System Technical Documentation, where we’ll explore how organizations can create and maintain detailed technical documentation to support transparency, troubleshooting, effective oversight, and long-term lifecycle management of AI systems.

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