A control model for identity, data, tools, evaluation and operations in enterprise AI. This guide gives buyers a structured way to frame the decision, identify risk and ask more useful questions of an internal team or delivery partner.
Treat the AI feature as a new access path
A model can expose or act on data through prompts, retrieval and tools. Apply the same identity and least-privilege principles used for the rest of the application.
Separate instructions, data and actions
System policy, retrieved content and user input should have clear boundaries. Tool execution must validate structured arguments on the server and recheck the user's permission for every consequential action.
Log what matters
Record model version, relevant context identifiers, tool calls, approvals, outcomes and user feedback without retaining unnecessary sensitive text.
- Identity and authorisation decision
- Model and prompt configuration version
- Sources used for grounded answers
- Tools requested and executed
- Approval, correction and escalation events
Evaluate continuously
Security testing includes prompt injection, data leakage, over-broad tool use and unsafe output handling. Regression tests should run when models, prompts, retrieval logic or source data change.