Responsible AI
Useful AI needs clear boundaries.
AI changes how a workflow behaves, who makes a decision and what can go wrong. We frame AI work around an explicit task, permitted data, evaluation criteria and a person accountable for the result.
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Review the information boundary
Map prompts, retrieved documents, tool results, logs and outputs. Agree the allowed providers and settings for sensitive information. Access to a source should not automatically grant permission to reveal it to every user.
- Permission-aware retrieval and output handling
- Provider and retention settings reviewed for the use case
03
Evaluate failure as well as usefulness
Representative tasks should include ambiguous inputs, unsupported requests and attempts to bypass instructions. Review correctness, inappropriate disclosure, bias concerns and the effect of a wrong answer in the actual workflow.
- Task-specific acceptance criteria
- Tests for misuse and unintended disclosure
- Human review of consequential outputs
- Regression checks after model or prompt changes
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Keep a path back to human judgement
Define fallback behaviour, monitoring and a route for users to question an output. Model and tool changes should trigger review where they affect the approved workflow. The NIST AI Risk Management Framework is a useful reference for structuring that discussion.
- A documented fallback and escalation route
- Reassessment when the system changes
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Share the context, constraints and questions you want the team to work through.
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