MN-3.2 Pre-trained models which are used for development are monitored as part of AI system regular monitoring and…
Pre-trained models which are used for development are monitored as part of AI system regular monitoring and maintenance. Pre-trained and transfer-learned models are treated as a monitored component in their own right, since their provenance and behaviour are not controlled by the deploying organisation.
Source: NIST AI RMF 1.0, MN-3.2. 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
- Monitoring records covering those models within regular maintenance · SIEM / log platform
periodic reviewEvidence produced at each review
- Assessment of risks carried over from the pre-training data and objective · HR system / LMS
governing documentDocuments that govern the control
- Identification of pre-trained models used, with version and provenance · Vendor register / contract repository
- The procedure followed when the upstream model is updated or withdrawn · Vendor register / contract repository
First move
Common gaps auditors find
- Pre-trained model treated as a fixed dependency and excluded from monitoring
- Provenance of pre-training data unknown and unrecorded
- No procedure for an upstream model being deprecated
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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