ISO 42001 (AI): A Simple Playbook to Get Audit-Ready

ISO 42001 gives you a management system for AI — policies, roles, risk, and monitoring.
Here’s a lean approach that works for startups and enterprises alike.

1) Build a living model inventory

Track every model in one list:

Business purpose, owners, data sources, training set origin
Model type (LLM, classical ML), deployment surface (app, bot, workflow)
Risks: safety, bias, privacy, security, legal
Controls: prompts/guardrails, evaluation, red-team notes
Status + change history

Keep it in SharePoint with required columns; export to PDF monthly for evidence.

2) Right-size the risk process

For each model:

Threats: jailbreaking, data leakage, bias, hallucination risk, prompt-injection.
Controls: content filters, retrieval isolation, data masking, rate-limits, human-in-the-loop.
Testing: pre-release eval set, abuse cases, red-team results.
Decision: go / no-go with accountable approver.

3) Human oversight is a must

Define when humans have to review outputs (e.g., safety or financial impact).
Log approvals in Teams/Approvals and store snapshots in SharePoint.

4) Operational monitoring

Usage metrics & drift indicators
Incident route for harmful outputs
Periodic re-evaluation cadence
Clear rollback/disable procedure

5) Evidence with Microsoft 365

Power Automate pulls monthly snapshots: inventories, evaluation results, approvals
SharePoint stores artefacts with retention & versioning
Purview applies sensitivity/retention labels to AI artefacts
Entra ID reports prove role-based access and reviews