In 2025, AI adoption moved faster than any organization's ability to govern it, and that gap is now the single biggest source of unmanaged risk in modern data programs. Teams shipped models, agents, and automated decisions into production long before the guardrails caught up, leaving privacy exposure, compliance blind spots, and eroding trust in their wake. Doing data right in 2026 means closing that gap on purpose, not by accident.

This session lays out a practical approach to governing AI at the speed it's actually being deployed. Drawing on emerging standards like the NIST AI Risk Management Framework and the EU AI Act, Kevin walks through how to identify, measure, and mitigate AI risk before it becomes a liability, and how to embed those controls into the systems and workflows teams already use. The focus is on what works in the field, not theory.

The core insight: governance is not a brake on innovation but the mechanism that lets it scale safely. Attendees will leave with a usable framework for building trust into automated systems, turning "responsible AI" from a compliance checkbox into a durable advantage with customers, regulators, and partners.