MS-3.1 Approaches, personnel, and documentation are in place to regularly identify and track existing…
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 identification continues after deployment with assigned personnel and a tracking record, so risks that emerge in real use are captured rather than only those anticipated at design.
Source: NIST AI RMF 1.0, MS-3.1. The statement above is our statement of the clause, read from the published text and cited so you can check it against the official text, not the instrument verbatim. Open NIST AI RMF 1.0 in the compliance library.
These are the artefacts auditors commonly ask for on this requirement, our guidance rather than text from the standard. Your assessor decides what is sufficient in your context, and asks for samples across the period when a report covers one.
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
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…