Govern the use case, not just the model
AI governance becomes manageable when policies connect to real workflows. The same model may be low risk when drafting an internal summary and high risk when recommending a client-facing financial decision.
Maintain an inventory of approved use cases, owners, data classes, tools, model providers, and prohibited actions.
Minimum practical controls
A small organization does not need a large committee to start, but it does need clear ownership and repeatable evidence.
- Role-based access and least privilege
- Approved data sources and retention periods
- Human approval for consequential actions
- Vendor and subprocessor review
- Prompt, model, and output version records
- Usage, cost, quality, and incident monitoring
- A documented pause, rollback, and notification process
Review on a schedule and after change
Reassess a use case when its model, prompt, data source, permissions, action set, or business purpose changes. Review higher-risk uses more frequently and record the decision.
Governance should enable safe learning. Clear boundaries make it easier to run a useful pilot because teams know what is permitted and what evidence is required to move forward.