Watch On Demand: Your AI is in Production. Do You Know What It’s Doing?

Most enterprise AI governance programs were built for deployment — not for what comes after. Once AI is live at scale, new questions emerge: Is it still behaving as intended? Who’s responsible when something changes? Can you prove to regulators that you’re watching? This session calls out the gap between shipping AI and actually governing it, and outlines what post-deployment accountability really requires.

• Governance doesn’t end at go-live. Most programs are built to get AI deployed, not to oversee it once it’s running.
• Three things most enterprises are missing: continuous monitoring infrastructure, defined human oversight practices, and clear organizational ownership of AI behavior.
• Behavioral drift is a real risk. AI systems can degrade or shift after launch — and most organizations have no process to catch it.
• Audit readiness requires proof of ongoing oversight, not just a pre-deployment approval.
• CIOs must treat deployment as the starting line, not the finish line, of AI governance.