Improvement
Correct failures, learn from incidents and audits, and continually improve the AI management system.
How to implement it
- 01Define how AI nonconformities are recorded, assessed, corrected, and verified.
- 02Investigate root causes for AI incidents, control failures, policy breaches, assessment gaps, and supplier issues.
- 03Assign corrective actions with owners, due dates, evidence expectations, and verification steps.
- 04Feed lessons learned into policy updates, lifecycle gates, model monitoring, training, and supplier requirements.
- 05Review whether improvement actions reduce residual risk rather than only closing tickets.
Evidence to keep
- - Nonconformity register
- - Root cause analyses
- - Corrective action records
- - Updated policies and procedures
- - Verification evidence
Common mistakes
- - Closing findings without proof of effectiveness
- - Treating AI incidents as one-off product bugs
- - Not updating risk assessments after failures
Policy templates for this control
Use these starting documents to turn the control into evidence. Adapt each template to your scope, systems, legal obligations and actual operating process.
AI use and governance policyUse for ISO 42001, AI governance, employee AI use, data handling, human review, and AI supplier risk.Incident response policyUse for ISO 27001 A.5.24 to A.5.28, SOC 2 incident response, PCI DSS 12.10, and security event handling.Data breach response policyUse for ISO 27001 A.5.24 to A.5.28, SOC 2 incident handling, privacy notification, and PCI DSS requirement 12.10.Logging and monitoring policyUse for ISO 27001 A.8.15, A.8.16, A.8.17, SOC 2 Security, and PCI DSS requirements 10 and 11.
Open the control-to-policy map