MS-4.3 Measurable performance improvements or declines based on consultations with relevant AI actors including…
Measurable performance improvements or declines based on consultations with relevant AI actors including affected communities, and field data about context-relevant risks and trustworthiness characteristics, are identified and documented. Change in performance over time is measured against a baseline using field data and consultation, so decline is detected as decline rather than absorbed as normal variation.
Source: NIST AI RMF 1.0, MS-4.3. 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
- Consultation records with AI actors and affected communities on observed change · Document repository
governing documentDocuments that govern the control
- Baseline measures for the trustworthiness characteristics being tracked · Policy repository / GRC workspace
- Field data showing performance over time against that baseline · Policy repository / GRC workspace
- Documented identification of improvement or decline and the action taken · Document repository
First move
Common gaps auditors find
- No baseline, so change cannot be identified
- Field data collected but never compared across periods
- Decline attributed to data quality without investigation
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.
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