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Most AI infrastructure was built for speed. The assumption baked into almost every RAG platform available today is that the team deploying it is moving fast in a low-stakes environment, optimising for iteration velocity above everything else.
That assumption breaks in regulated industries.
When your AI system retrieves across multiple datasets that each carry different sensitivity levels and different access permissions, the retrieval layer itself becomes a compliance surface. A shared RAG instance can join records on a common key at query time and assemble information that no single dataset contained — information that crosses information barriers, exposes AML-sensitive client status, or surfaces compensation data to someone never authorised to see it.
No individual access control was designed to catch this. Standard RAG implementations do not monitor for it.
We built Katara because we kept seeing the same gap: teams doing everything right on the data governance side, then deploying AI infrastructure that undermined it.
Katara is the infrastructure layer where data isolation, PII monitoring, role-based access, and audit-ready logging are not features you configure after the fact.
They are the foundation.
