About

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.

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.
The 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.

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.

Takeaways

• AI governance is a context problem, not just a policy problem. Most regulatory AI risk in terms of hallucinated outputs, unexplainable decisions, and inconsistent metrics originate in missing or fragmented data context, not inadequate models. Delegates will leave understanding where the real gap is and why it resists point solutions.
• Lineage, business meaning, and policy can be encoded once and reused everywhere. The traditional approach rebuilds governance for every use case. A governed context layer inverts that: context built for regulatory reporting reuses for audit automation, fraud, and customer analytics. Each new use case is cheaper and faster than the last and every output carries an auditable provenance chain.
• “Explainable AI” requires explainable data and that standard is now regulatory, not just aspirational. BCBS 239, GDPR Article 30, EU AI Act, and SR 11-7 all impose traceability requirements that voluntary data cataloguing cannot satisfy at scale. Delegates will understand the capabilities within a production-grade, regulator-ready context architecture actually looks like.