Human oversight
Assign a responsible reviewer, define which outputs need approval and ensure people can stop or override automated actions. Review is only useful if people have time, information and authority to act.
Data governance and privacy
Map the information entering and leaving the system. Ask about access, retention, deletion, model training use and subprocessors. Refer jurisdiction specific obligations to appropriate privacy and legal specialists.
Security
Review identities, permissions, secrets, logging, incident response and boundaries around tools and actions. Test representative misuse and failure scenarios in an authorized environment.
Model limitations
Document known failure modes and where evidence is missing. Require a safe response when a task cannot be completed reliably. Explain limitations to the people relying on outputs.
Monitoring and documentation
Define ongoing checks, incident records, review intervals and escalation. Retain the evidence supporting a decision, together with model versions, configurations and test conditions.
Accountability and change management
Decide who owns approval, updates and rollback. Reassess when the vendor, deployment, data, model or use case changes.
Vendor dependency
Ask how data and configurations can be exported, what changes require notice, and what happens if service ends. Consider a practical exit plan.
Reference: the voluntary NIST AI RMF Playbook organizes suggested actions around Govern, Map, Measure and Manage. NeutralIntelligence does not claim NIST certification, endorsement or accreditation.