The Compliance Case for a Single Governed MCP

As AI agents become standard in enterprise workflows, most organizations haven't reckoned with what happens when those agents query Salesforce, Snowflake, Tableau, and a dozen other systems — each through a separate MCP. The result is a fragmented audit trail, no single record of who accessed what data or how an answer was generated, and a growing compliance exposure under GDPR, HIPAA, SOX, and the EU AI Act. In this session, we'll examine why multi-MCP architectures create compliance gaps that traditional data governance tools weren't built to address — and how Simba Intelligence's governed semantic layer solves the problem at the access point. With a single MCP, every AI query is logged with full user attribution, deterministic lineage, and policy enforcement built in — before the model ever sees the data. We'll walk through real-world scenarios in financial services, healthcare, and ISV environments where audit-ready AI isn't optional — and show how Simba Intelligence delivers governance without sacrificing architectural control or LLM flexibility.