Monitoring & Drift
Operational controls for monitoring & drift, with maturity levels, evidence requirements, and implementation guidance.
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What applies to me? →6 controls
AI Performance Baseline
Establish documented, quantified performance baselines for live AI systems against which ongoing performance can be compared.
Model Drift Detection
Monitor live AI systems for data drift, concept drift, and shifts in outputs that signal degraded or changed model behavior.
AI Bias and Fairness Monitoring
Continuously monitor AI system outputs for discriminatory patterns across protected demographic attributes in live use.
AI Output Anomaly Detection
Automatically detect unusual, unexpected, or potentially harmful AI outputs in live use for investigation and response.
Continuous Model Evaluation
Run ongoing automated tests on reserved test data and deliberately tricky inputs to continuously measure live model performance.
Behavioral Anomaly Detection for Agentic Systems
Monitor AI agents for unexpected action sequences, data or system access, and behavior inconsistent with assigned tasks.
Monitoring & Drift, tracked weekly
New monitoring & drift controls and the regulatory developments driving them, plus everything else changing in AI governance. Every Thursday.