Tier 2: Evidence-grade controls

AI model monitoring and drift management

Continuous oversight to detect performance degradation, data shifts, and unintended outcomes, with defined actions when thresholds are breached.

Board-defensible evidence

• Monitoring plan defining what is monitored (accuracy, drift, bias signals, latency, error types), how often, and the thresholds that trigger investigation or rollback, with named owners.
• Dashboards and alerting evidence showing real-time or periodic monitoring outputs, alert history, and ticket links documenting investigations and corrective actions taken.
• Drift analysis records showing what changed (data distribution, population, environment), when the change began, and how impact to outcomes was measured and reported to oversight teams.
• Operational playbooks showing what happens when thresholds are breached, including escalation paths, decision rights to pause the model, and communication requirements to stakeholders.
• Periodic performance review and revalidation records showing sign-off to continue operation, including documented rationale when operating near tolerance limits.

Why this matters

Post-incident questions are simple: what did you monitor, when did you know, and what did you do about it.