Most AI governance frameworks are written for products where a bad output is embarrassing. In financial services and commodity trading, a bad output can trigger regulatory exposure, erode client trust, or generate incorrect positions.
This talk moves past the checklist approach to examine what AI governance actually looks like when deployed in high-stakes production environments. Drawing on experience deploying AI in an energy trading platform processing nearly $100B in commodity value daily, the session covers: auditability requirements for AI decisions that affect financial positions; model drift detection strategies that catch degradation before users do; rollback protocols that don't require taking down the entire system; and how to design human-in-the-loop checkpoints that preserve oversight without killing the user experience.
The talk is practitioner-led and non-commercial, designed for risk leaders, product security teams, and technology executives who are past the policy stage and trying to operationalize governance in production AI systems.
Key takeaways: a practical architecture for AI auditability in high-value transaction environments; how to detect and respond to model drift without disrupting operations; design patterns for human oversight that scale; and where most enterprise AI governance frameworks break down in practice.