MS-3.1 Approaches, personnel, and documentation are in place to regularly identify and track existing, unanticipated, and emergent AI risks based on factors such as intended and actual performance in deployed contexts
Approaches, personnel, and documentation are in place to regularly identify and track existing, unanticipated, and emergent AI risks based on factors such as intended and actual performance in deployed contexts. Risk ide
4
artefacts
0
held by a system
1
at each review
hard
to go live
Document repository
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 tracking record showing risks identified after deployment · Source control / CI pipeline
governing documentDocuments that govern the control
- The approach for identifying emergent and unanticipated risks in deployment · Policy repository / GRC workspace
- Personnel assigned to that identification and tracking · Document repository
- Comparison of actual against intended performance in the deployed context · Document repository
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
- Risk identification treated as a design-phase activity that ends at launch
- Emergent risks noted in incident tickets but never entered as risks
- No one assigned, so identification depends on something going visibly wrong
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 sheetMS-2.13 Effectiveness of the employed TEVV metrics and processes in the MEASURE function are evaluated and documented · MS-3.2 Risk tracking approaches are considered for settings where AI risks are difficult to assess using currently available measurement techniques or where metrics are not yet available