AI monitoring, drift detection & logs

ISO 42001 requires you to actively monitor live AI systems, detect issues such as drift and anomalies, and maintain audit-ready logs that document what happened, when, and who responded. For audit purposes, monitoring, drift detection, and logging should be tightly linked, documented, and mapped to your AIMS controls. The above requirements are supported by Annex A.6.2.6 and Annex A.6.2.8. Monitoring and drift detection Monitoring under ISO/IEC 42001 must extend beyond system uptime to encompass data behavior, model performance, and real-world outcomes. This aligns monitoring with the standard’s expectations around continuous improvement, accountability, and harm prevention. Organizations typically track data quality and input shifts through automated checks that detect changes in input distributions, missing values, or out-of-policy data relative to training baselines. Performance and fairness are monitored using ongoing accuracy, error, utility, and bias indicators, with alerts triggered when metrics breach defined thresholds. Behavioral anomalies, such as unexpected patterns, adversarial activity, or security-relevant deviations, should also be detected at runtime. Drift detection should be treated as a formal control rather than an informal practice. This includes documenting the methods used (for example, statistical tests or performance decay thresholds), defining review frequency, and establishing clear escalation paths. Logging requirements for ISO/IEC 42001 Logs form the primary audit trail for demonstrating “who did what, when, and with which model and data.” Logging should align with ISO 27001-style security expectations and applicable privacy requirements. Effective AI logs typically capture context, including timestamps, system and environment identifiers, lifecycle stage, and model version. They also record actors and actions, including user or service identity, role, actions performed, and justifications where required. Inputs, outputs, and system state should be logged at an appropriate level, along with approvals, alerts, rejected actions, and links to incidents or corrective actions. Logs should be append-only, integrity-protected, and governed by retention and deletion rules that align with legal and organizational policies. Turning monitoring and logs into audit evidence For ISO/IEC 42001, auditors will expect monitoring and logging to be implemented as structured controls, not ad hoc tooling. They will typically review both documented procedures and real operational data. Common evidence includes documented monitoring procedures describing metrics, thresholds, tools, review cadence, and escalation workflows. Historical drift and anomaly records, including alerts, investigations, retraining, and rollback decisions, are also important. Additional evidence may include logging policies, retention schedules, proof of tamper resistance, and dashboards or control trackers used for ongoing AI oversight.