MN-3.2 Pre-trained models which are used for development are monitored as part of AI system regular monitoring and maintenance
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, si
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- The procedure followed when the upstream model is updated or withdrawn · Vendor register / contract repository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.
Build my evidence sheetMN-3.1 AI risks and benefits from third-party resources are regularly monitored, and risk controls are applied and documented · 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