Data & Knowledge Intelligence

Data Governance for AI

Before an AI system is pointed at a corpus nobody has classified, which is the point at which governance stops being theoretical.

Governance for AI covers classification, retention, and access rules applied to the data AI systems reach — including, importantly, what must not be indexed at all.

The exclusion list is the part most often omitted and most often regretted, because an assistant that answers helpfully from material it should never have seen is difficult to explain afterwards.

What the work includes

  • Classification of sources by sensitivity and permitted use
  • Explicit exclusion of material that must not be indexed
  • Retention rules applied to derived artifacts, not only to source data
  • Review cadence, since access granted for a project outlives the project
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// Common Questions

Questions
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Common questions about Data Governance for AI.

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Knowing what is in the corpus an AI system can reach, who may see each part, and what must never be surfaced. Traditional governance often stops at the system boundary; an AI layer reads across boundaries, which is where unclassified material becomes a problem.

Because pointing a retrieval system at an unclassified corpus is how sensitive material reaches people who should not see it — and the first evidence is usually someone receiving an answer they should not have. Classification after the fact does not undo the disclosure.

No. Scope the corpus to material that has been classified and expand as more is covered. That is slower than opening everything at once and considerably faster than recovering from a disclosure.

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