MN-4.1 Post-deployment AI system monitoring plans are implemented, including mechanisms for capturing and evaluating input from users and other relevant AI actors, appeal and override, decommissioning, incident response, recovery, and change management
Post-deployment AI system monitoring plans are implemented, including mechanisms for capturing and evaluating input from users and other relevant AI actors, appeal and override, decommissioning, incident response, recove
4
artefacts
0
held by a system
1
at each review
hard
to go live
Policy repository / GRC workspace
where the evidence lives
teal = a system already holds it · olive = produced at each review
system holds itEvidence a system already holds
none for this control
periodic reviewEvidence produced at each review
- The appeal and override mechanism and records of its use · Document repository
governing documentDocuments that govern the control
- The implemented post-deployment monitoring plan and its scope · Policy repository / GRC workspace
- Mechanisms for capturing and evaluating user and AI actor input · Document repository
- Incident response, recovery, change management and decommissioning arrangements for the system · Policy repository / GRC workspace
First move
This control is evidenced by people and documents, not systems. Put the document under version control with an owner and review date, and log each review as a record with reviewer and date. Do not try to automate it.
Common gaps auditors find
- Plan documents each element but only performance monitoring is running
- Appeal and override present in the plan with no implemented mechanism
- Change management for the model handled outside the plan by the platform team
Do this for your whole sheet
Paste the rows you run your controls from and get this mapping for every control at once, with the periodic-review ones flagged and a first move per row. No account for the first run.
Build my evidence sheetMN-3.2 Pre-trained models which are used for development are monitored as part of AI system regular monitoring and maintenance · MN-4.2 Measurable activities for continual improvements are integrated into AI system updates and include regular engagement with interested parties, including relevant AI actors