This representative Pūnaha workflow begins with a difficult internal question: what guidance applies when restricted information may be used with artificial intelligence?
The request is structured before any model is called, so the intended decision, audience and information boundary remain visible.
Pūnaha checks the permitted tenant and knowledge scope.
In this fictional example, the requester can use the approved policy, information security and privacy collections, while material outside that boundary is excluded.
Three retrieval branches then run in parallel.
Hybrid search finds relevant extracts from the fictional acceptable-use policy, information-classification standard and privacy-assessment guide.
Exact terms and semantic meaning both contribute to the ranked context.
A configured model prepares an evidence brief from those extracts.
It separates supported guidance, unresolved questions and recommended human decisions, while retaining links back to the material used.
A grounding gate checks that the important statements have supporting evidence.
An unsupported statement is routed back for revision instead of quietly passing to the reviewer.
The workflow pauses for an accountable owner.
They can inspect the proposed brief and its sources, amend it, approve it or reject it.
This illustrative run records approval with a clarification.
Finally, the approved structured result becomes available to the next configured step, and the workflow version, stages and decision remain visible in execution history.
It is a traceable decision aid, not an automatic policy decision.