The short answer
Close an AI workflow incident by fixing its scope and timeline, containing further actions, reconciling every potentially affected external and internal effect, restoring through tested controls, documenting cause and contributing conditions, assigning prevention work, and accepting residual risk. A model or service returning to normal is not incident closure.
Key takeaways
- Reconcile affected actions before restoring broad authority.
- Separate immediate cause, contributing conditions, and impact.
- Require tested recovery and owned prevention items.
Freeze the incident boundary and preserve evidence
Record detection source, first known affected event, last known good state, workflows/models/providers/configurations involved, properties and record types potentially affected, actions paused, evidence locations, and incident owner. Preserve prompts, tool calls, retrieved sources, policy versions, approvals, receipts, and system logs as permitted by privacy and security policy.
NIST’s voluntary AI RMF describes ongoing monitoring, incident response, recovery, and change management as risk-management activities. It does not define a property-management incident standard or certify a particular workflow.
Reconcile effects, not just model outputs
Build a population of every potentially affected action, including no-action outcomes. Compare the governed source record, AI proposal, approval state, external execution, receipt, current provider state, and downstream record.
| Gate | Evidence | Cannot be replaced by |
|---|---|---|
| Contained | Authority removed, queue paused, or condition blocked | A verbal instruction to be careful |
| Population reconciled | Every affected action has disposition | A sample with unknown denominator |
| Recovered | Corrected path tested on representative failure modes | Service availability alone |
| Restored | Explicit authority decision and monitoring window | Automatic restart |
| Learned | Cause, contributing conditions, and owned changes | Generic “model error” label |
| Closed | Residual exceptions and risk accepted by owner | No new alerts for a day |
Restore authority in bounded stages
Test deterministic validations with ordinary software logic where possible and use human review for consequential uncertain decisions. Re-enable the smallest action class or property scope first, define monitoring signals and rollback triggers, then expand only under the approved recovery plan.
Track long-term fixes separately from incident status with owners and due dates. If a fix changes prompts, models, tools, data sources, permissions, or provider behavior, route it through the appropriate change-acceptance and test-evidence process.
Reconstruct the affected population when telemetry is incomplete
Union candidates from the workflow engine, model or provider, system of record, external provider, approval records, and support evidence. Join by the stable business action where possible and preserve every contributing identity.
Label confirmed affected, confirmed unaffected, possible, duplicate representation, and unknown. Absence from a broken log cannot support exclusion; carry uncertainty into compensating review and the residual-risk decision.
Operational checklist
Mark your progress, then save a working copy. Selections reset when you leave this page. A checked box is not an approval or evidence of completion.
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Incident boundary and evidence frozen
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Automation authority contained
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Potentially affected action denominator built
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Each effect reconciled or owned
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Recovery path tested
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Restoration scope and rollback triggers approved
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Cause, changes, and residual risk recorded
0 of 7 marked
Edge cases
- The affected denominator cannot be reconstructed: state the evidence limit and use bounded source comparisons.
- A provider changed behavior without a local deployment: include provider context in cause and acceptance testing.
- Some actions cannot be reversed: record compensating actions and recipient impact separately.
Sources and references
Follow each source to check the underlying claim. Access checks and professional review are different steps.
1. Primary source · National Institute of Standards and Technology
AI Risk Management Framework CoreThe voluntary AI RMF describes governed roles, documented risks, monitoring, incident response, recovery, and change management. It is not a property-management certification.
Source checked 2026-09-18
Automated source-access check: 2026-09-18.
Continue the workflow
Review AI workflow failure modes before launchReconstruct an AI incident population when logs are incompleteAI model and provider change acceptance gateRevision history
2026-09-18
Initial Phase 3 operational article with a distinct decision artifact, failure states, source-scope notes, and AI-assisted technical review.