{"id":2034,"date":"2026-09-28T11:08:39","date_gmt":"2026-09-28T11:08:39","guid":{"rendered":"https:\/\/datadecoded.com\/mcr\/?post_type=seminar&p=2034"},"modified":"2026-09-30T14:58:25","modified_gmt":"2026-09-30T14:58:25","slug":"the-missing-layer-why-ai-fails-governance-and-what-context-does-about-it","status":"publish","type":"seminar","link":"https:\/\/datadecoded.com\/mcr\/seminars\/the-missing-layer-why-ai-fails-governance-and-what-context-does-about-it\/","title":{"rendered":"The Missing Layer: Why AI Fails Governance and What Context Does About It"},"content":{"rendered":"

An earlier keynote set the scene: context is the imperative. This session makes it operational and specifically for the organisations where getting it wrong means regulatory consequences, not just bad analytics.<\/p>\n

Financial services firms, and increasingly all regulated industries, are discovering that their AI governance failures are not model failures. They are context failures. Business definitions that vary by team. Lineage that is reconstructed after the fact, if at all. Policies encoded in documents no agent can read. AI outputs that are confident, fast and indefensible.
\nThe Teradata Context Engine addresses this directly: a governed context layer that encodes business meaning, relationships, lineage, and policy once, and more importantly makes it available to every agent, every tool, every use case. Context as infrastructure, not afterthought.<\/p>\n

This session unpacks how that works in practice: the architecture, the regulatory use cases, and the compounding value that accrues when governance is built into the context layer rather than bolted on after deployment.<\/p>\n

Takeaways<\/strong><\/p>\n