AI Control Library / CF-10: Recourse & Incident Response
Designation: Core | Control types represented: Operational
Control requirement
Review AI incidents and material near misses to identify contributing conditions, failed assumptions and practical improvements. Assign and verify corrective actions across technical, operational and organizational causes.
Why this matters
Recovery restores service but may leave the conditions that produced the event intact. Learning should examine evidence and system incentives instead of stopping at model error or individual blame.
How the control types work together
Operational controls establish the assigned responsibilities, review processes and response decisions described here. Their effectiveness depends on performing the work and using evidence to guide action, rather than approving a document alone.
When it applies
Closed and stabilized AI incidents, consequential near misses and recurring patterns warranting structured review.
Define the assessed boundary, dependencies and evidence limitations. Tailor depth and review frequency to potential impact, and document why a particular safeguard or exclusion is appropriate to the actual deployment.
Ownership
The incident review owner coordinates response and business stakeholders with relevant technical and domain expertise.
Implementation guidance
- Preserve a supported timeline and evidence, distinguishing known causes, contributing factors and unresolved hypotheses.
- Examine model, data, authority, interface, supplier and human conditions, including why safeguards or escalation failed.
- Select proportionate improvements with accountable owners, deadlines and verification criteria.
- Track completion, retest relevant capabilities and update risk, training, plans and evaluation cases where needed.
How to verify it
Use representative records and authorized controlled tests. Identify the relevant version, operating conditions and expected result before testing, and retain enough evidence to support the conclusion.
| Test | Expected result |
|---|---|
| Inspect a review conclusion | Evidence supports the identified cause and uncertainty remains explicit. |
| Trace an improvement | Its owner and effectiveness criterion are recorded. |
| Review recurrence | Earlier actions are reassessed rather than assumed successful because tickets closed. |
Record a finding when required results cannot be demonstrated or practice differs materially from the approved conditions. Missing evidence limits the conclusion; it should not be treated as a successful test.
Evidence to retain
- Incident timeline and analysis
- Contributing-factor review
- Improvement plan
- Retest and program updates
Protect evidence according to its sensitivity, access needs and retention rules. Avoid unnecessary copies of personal information, secrets or confidential material, while preserving the records needed for authorized review.
If the control fails
Escalate unresolved high-impact causes or ineffective improvements to business and risk owners. Maintain relevant operating restrictions until corrective capability is established.
Example in practice
Hypothetical example: A reviewer approved an unsafe action under excessive workload. The review changes queue limits and interface evidence as well as training, then tests whether meaningful review is restored.
Related controls
- CF04-006: AI Audit Finding Remediation
- CF07-009: Human Oversight Adequacy Monitoring
- CF10-004: AI Incident Response Testing & Exercises
Framework mappings
- NIST AI RMF (MANAGE 3.1)
- EU AI Act
- ISO 42001 (Cl. 10.1)
Review and maintenance
Review at the interval approved by the control owner and after relevant changes, incidents or failed assessments. Update affected procedures and configuration, address evidence gaps and verify corrective action before closing findings.
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