Turn AI risks into checks your team can use
Access rules, approval steps and clear records help reviewers see how a workflow behaves. Start with the action that could go wrong and the check that prevents or catches it.
Translate risks into gates
High-risk actions should never rely on model confidence alone. Add approval and policy layers where the cost of being wrong is material.
Translate risks into evidence
Keep the relevant inputs, source references, proposed action, reviewer decision and final result together. Avoid retaining sensitive information the review does not need.
Translate risks into scope limits
Agents need explicit bounds for tools, destinations, data classes, and execution modes so failures stay contained.
What the reviewer needs to see
- Risk points in the workflow and what type of control protects each one
- What actions remain autonomous and what actions require human approval
- How prohibited behavior is tested before release and monitored after launch
- What evidence operators and reviewers can inspect during exceptions or incidents