AI use controls define the conditions within which an AI-assisted workflow is permitted to operate. They cover input, sources, output, authority, human review, technical permissions, and stop conditions.
Worked example
Completed example — fictional protocol reviewer
| Boundary | Permitted use |
|---|---|
| Documents | Approved requirements and draft protocol for one defined system |
| Sources | Access-controlled versions selected for that review |
| Output | Candidate gaps with source references and uncertainty |
| Human role | Qualified reviewer checks the source and decides disposition |
| Prohibited action | Approving the protocol or changing controlled requirements |
| Technical permissions | Read documents; save a draft review result in the authorized area |
| Stop condition | Missing source, unauthorized content, unsupported version, or service failure |
| Change trigger | Model, prompt, parser, retrieval, permissions, or workflow change |
Why human review is not enough by itself
A reviewer can miss errors, follow a convincing recommendation, or lack access to the source. Test the review workflow with realistic time pressure and difficult cases. Make rejection and escalation easy. Ensure technical permissions prevent the assistant from performing prohibited actions.
Contrasting example
A deviation agent that can create tasks needs additional action boundaries: allowed destinations, duplicate prevention, confirmation, logging, and recovery. A text-generation boundary does not address those actions.
Reusable worksheet
Write allowed users, inputs, sources, tasks, outputs, actions, permissions, required human checks, record treatment, unsupported cases, stop rules, and reassessment criteria.
Limit: A prompt saying “do not approve” is weaker than a system that provides no approval permission. Use controls at the layer where the failure can occur.
Questions and discussion
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