AI can prepare a polished draft before a reviewer has established whether it is true. For compliance work, useful safeguards must help the reviewer compare that draft with the records.
Three checks matter most: sources, gaps, and approval.
Check the source
A draft should point to the material used to support a factual claim. The reviewer needs to open that material and assess whether it supports the wording.
The presence of a citation is not enough. A document might be outdated, cover a different system, or describe a policy rather than a completed activity.
Make missing information visible
An agent should flag questions the available records cannot answer. For example, an AI governance inventory might list a tool without identifying its business owner. That is a question for the team, not an invitation to choose a likely name.
Keep missing facts separate from proposed recommendations. Both can be useful, but they serve different purposes.
Keep approval in the workflow
A reviewer should see the proposed change before accepting it. They should be able to correct the wording, ask for more evidence, or reject the proposal.
Approval is meaningful only when the reviewer has enough time and knowledge to check the result. A sequence of automatic accept clicks is not a substitute for review.
Where CASK fits
CASK puts the AI agent, records, drafts, and proposed edits in one desktop workspace. Its source checks and review steps help reduce hallucination and fabrication by making unsupported claims easier to find.
People still check the evidence and decide what becomes part of the work. The quality of the result depends on the records, the model, the task, and that review.
Can we use this approach without CASK?
Yes. These are useful evaluation criteria for any AI-assisted workflow. Test them using records and questions your team can verify.
Can Truvara help deliver the work?
Yes. Our practitioners use CASK for agreed projects and ongoing delivery, with review responsibilities set at the start.