Define the decision, owner and expected outcome.
Every AI use case needs a business owner, permitted users, success criteria, failure boundaries and a clear statement of what the system is not allowed to decide.
Practical governance framework
A practical governance model for private AI.
Private AI becomes trustworthy when business ownership, data controls, model quality, action permissions and operational evidence are designed as one system. The framework below is intentionally concise enough to use in a real deployment.
Control the context before optimizing the model.
Identify approved sources, sensitivity, access rules, retention and transfer boundaries. Retrieval should enforce the same permissions as the underlying business systems.
Evaluate the complete workflow, not only the model.
Reference tasks must test retrieval, citations, tool use, latency, abstention and edge cases. Model or prompt changes should be reviewed against the same regression set.
Bound autonomy through tools and approval gates.
Agents receive only the tools required for their role. Sensitive actions require explicit approval, limits, traceability and a reliable way to stop or reverse the workflow.
Operational evidence
The governance pack should be usable during normal operation.
- 01AI inventory
Use case, owner, users, data, model, tools, suppliers and deployment location.
- 02Control matrix
Roles, permissions, approval points, prohibited actions and escalation paths.
- 03Evaluation record
Reference scenarios, acceptance thresholds, known limitations and regression results.
- 04Change record
Model, prompt, retrieval and tool versions with approval and rollback information.
- 05Incident procedure
Detection, containment, evidence preservation, communication and corrective action.
Translate the framework into a working private AI architecture.
MadAI Systems connects governance requirements with identity, retrieval, model routing, agent controls and operational monitoring.