Compliance Confident: When RAG Meets Real Enterprise Data

You're building AI on top of enterprise data, and you're likely using retrieval-augmented generation as part of that stack. RAG helps ground models in relevant context, but relevance alone breaks down when governance, auditability, and regulatory compliance enter the conversation. Before a model can generate a trustworthy answer, the data feeding it needs to be governed, contextualized, and controlled at the source. That means resolving business logic, enforcing permissions, and applying semantic meaning before anything reaches the AI — not after. In this session, we focus specifically on governance at the data connectivity, access, and preparation layer: what it requires, why most AI stacks don't have it, and what changes when you put it in place. We'll cover:

• Why governance enforced at the data layer is architecturally different from governance bolted on after the fact

• What deterministic, auditable AI actually requires at the connectivity and access layer

• How a governed semantic layer prepares enterprise data so AI can use it reliably and responsibly

• Where RAG fits in a governed AI stack and where it needs upstream governance to hold up in production

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