Governing Autonomous Systems
Define what an AI workflow may do, who reviews it, and how your team stops or recovers it.
Connecting a model to business data and tools creates a new operating responsibility. The risk depends on what the workflow can access, what it can change, and what happens when a result is wrong.
Start by defining the workflow boundary, the named owner, and the decisions that require a person.
Build Policy Into Execution
Translate your policies into controls that operate with the workflow:
- Input, output, and action checks before a tool is used
- Least-privilege access to data and tools
- Approval steps for important or unusual decisions
- Stop, override, retry, and recovery controls with named owners
Give Reviewers Useful Evidence
Record the information your team needs to evaluate the workflow: relevant inputs, policy checks, tool calls, outputs, approvals, exceptions, and outcomes. Set retention and access rules so the record does not expose unnecessary sensitive data.
A useful record shows what happened, which rule allowed it, who approved it, and how the system responded when a step failed.
Start Narrow, Then Expand With Evidence
Begin with one defined workflow and limited permissions. Test expected paths, unsafe requests, unavailable tools, bad data, and low-confidence outputs before release.
Broaden the workflow only when results, incident history, user feedback, and control reviews support the change.
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