Approval to use an AI system should not be permanent by default. Teams need clear conditions for reconsidering a use case when its purpose, performance, provider, data, risk, or operating environment changes.
Define exit conditions before they are needed
Set practical triggers such as unresolved material incidents, monitoring outside approved thresholds, unsupported model changes, loss of required controls, unacceptable data handling, or a use case expanding beyond its reviewed purpose.
Use graduated responses
Not every issue requires retirement. Depending on severity and reversibility, the response may be tighter limits, additional human review, temporary pause, remediation, reapproval, provider change, or retirement.
Keep evidence with the decision
Record the trigger, evidence considered, decision owner, restrictions, remediation actions, review date, and conditions for any restart. Preserve enough history to explain why the operating state changed.
Coordinate specialist authority
Governance teams can organize the process, but legal, regulatory, clinical, financial, employment, safety, privacy, accessibility, and other professional determinations remain with appropriately qualified decision-makers.
Related knowledge
Use this with AI Governance Operating Reviews, AI Monitoring Thresholds, AI Use-Case Inventories, AI Testing Evidence, and AI Governance Control Owners.
References
This resource supports operational governance. It does not establish legal compliance or replace qualified legal, regulatory, clinical, financial, employment, safety, privacy, accessibility, or other professional review.