From this article At Informatica World 2026, Informatica and Microsoft announced native integration of IDMC (Intelligent Data Management Cloud) into Microsoft Foundry via the Model Context Protocol (MCP). When an AI agent tries to pull data from a restricted table, the IDMC governance layer intercepts the call in under 100ms, blocks it, and returns a compliant alternative, all without the developer writing a single policy line. One Fortune 500 insurer went from a full freeze on agent deployments to 40+ agents in production in under three weeks once this was in place. This is the shift that unblocks enterprise AI at scale. For years, governance teams and AI engineers have been in a standoff: engineers want to ship, governance wants controls, and neither side has had a clean handoff. Embedding policy enforcement directly into the agent runtime via MCP removes that negotiation entirely. The Verdict: Organizations that still treat governance as a post-deployment audit step will keep watching their AI initiatives stall at the risk committee stage — this integration sets a new baseline for what "production-ready" means. Let's Discuss: 🏗️ If your organization deployed AI agents today, could your data governance stack tell you, in real time, what data each agent accessed and why? Or would that require a manual audit? 🤝 Who actually owns AI agent governance in your organization right now, the data team, the security team, or the AI engineering team? And is that a clean ownership, or a gap waiting to become an incident?

Hello All, I have around 10 years of experience as a data analyst and now I want to transition to data governance. Can someone suggest the roadmap ?

Is anyone using Collibra and Unity Catalog as their main data governance tools?