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This representative Pūnaha workflow begins with a difficult internal question: what guidance applies when restricted information may be used with artificial intelligence?

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The request is structured before any model is called, so the intended decision, audience and information boundary remain visible.

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Pūnaha checks the permitted tenant and knowledge scope.

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In this fictional example, the requester can use the approved policy, information security and privacy collections, while material outside that boundary is excluded.

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Three retrieval branches then run in parallel.

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Hybrid search finds relevant extracts from the fictional acceptable-use policy, information-classification standard and privacy-assessment guide.

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Exact terms and semantic meaning both contribute to the ranked context.

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A configured model prepares an evidence brief from those extracts.

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It separates supported guidance, unresolved questions and recommended human decisions, while retaining links back to the material used.

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A grounding gate checks that the important statements have supporting evidence.

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An unsupported statement is routed back for revision instead of quietly passing to the reviewer.

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The workflow pauses for an accountable owner.

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They can inspect the proposed brief and its sources, amend it, approve it or reject it.

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This illustrative run records approval with a clarification.

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Finally, the approved structured result becomes available to the next configured step, and the workflow version, stages and decision remain visible in execution history.

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It is a traceable decision aid, not an automatic policy decision.