An approval should not be treated as permanent when the system, its use, or its surrounding risk changes. Review triggers define the events that require an AI use case to be reassessed instead of relying on someone to notice informally.
Start with material changes
Potential triggers include a different model or provider, new data sources, expanded user populations, a new decision role, changed integrations, altered human oversight, significant performance changes, or deployment into a new business context. The relevant threshold depends on the use case and its consequences.
Include operational signals
Incidents, recurring complaints, unexpected outputs, control failures, monitoring findings, or evidence that assumptions no longer hold can justify review even when the technical configuration has not changed.
Define the route before the trigger fires
For each trigger category, identify who receives the notice, what evidence is required, whether use can continue during review, and who has authority to decide. High-impact contexts may require specialist review beyond the normal governance team.
Keep the decision traceable
Record what changed, why review was triggered, evidence considered, conditions imposed, and the resulting decision. NIST AI RMF 1.0 describes AI risk management as an ongoing lifecycle activity through its Govern, Map, Measure, and Manage functions.
Related knowledge
Use this with AI Governance Evidence Packs, AI Change Impact Reviews, AI Decision Logs, and AI Risk Acceptance Records.
References
This resource supports operational governance. It does not establish legal compliance or replace qualified legal, regulatory, clinical, financial, employment, safety, accessibility, privacy, or other professional review.