THE SHORT ANSWER
Monitor task quality, harmful failures, affected-group outcomes where relevant, human overrides, complaints, data and model change, security, latency and cost. Define thresholds, owners and actions before launch, then sample outputs and investigate signals in context.
Monitor outcomes and controls
| Category | Signal |
|---|---|
| Quality | Accepted error, groundedness and exception rate |
| People | Complaints, appeals, overrides and unequal outcomes |
| Operations | Latency, failure, cost and fallback use |
| Security | Unexpected access, tool use or data exposure |
| Change | Model, prompt, data, vendor or policy version |
Connect every signal to an action
A dashboard without a response owner is observation, not control. Define warn, pause and stop thresholds; identify who investigates, how affected work is corrected and when the system may return.
Logs and samples need context
Aggregate metrics can hide rare severe failures. Review representative samples and targeted high-risk cases, protect sensitive logs and avoid turning monitoring into unnecessary surveillance.
Reassess after meaningful change
- Changed purpose or affected population
- New data source or permission
- New model, prompt or agent tool
- Material performance shift
- Incident, complaint or legal change
- Vendor or dependency change
Monitoring feeds the AI incident response process and the next governance review.
Evidence & context: National Institute of Standards and Technology · International Organization for Standardization
Sources & further reading
- Artificial Intelligence Risk Management Framework (AI RMF 1.0)
National Institute of Standards and Technology. Voluntary, rights-preserving guidance organized around GOVERN, MAP, MEASURE and MANAGE. NIST was revising AI RMF 1.0 when checked on 28 September 2026, so organizations should verify the current version before formal adoption.
- NIST AI RMF Playbook
National Institute of Standards and Technology. Suggested actions for using AI RMF 1.0. It is voluntary, not a checklist or certification, and NIST states that it will be updated after the framework revision.
- ISO/IEC 42001 explained: What it is, why it matters, and how it works
International Organization for Standardization. Official overview of the AI management-system standard and its continual-improvement approach. Certification scope and a management system do not by themselves prove that a specific AI use is safe, fair or legally compliant.
Examples and exercises are illustrative unless attributed to a source. No independent expert review is claimed.
A correction, a counterexample or an experience worth sharing?
Join the conversation ↗