AI is making decisions, accessing sensitive data, and taking actions across the enterprise. But when something goes wrong, who owns the risk?
As AI becomes embedded in business applications, employee workflows, and autonomous agents, traditional governance models are being pushed beyond their limits. Questions around ownership, accountability, data usage, vendor responsibility, and regulatory compliance are becoming increasingly difficult to answer… especially as AI systems operate across organizational and technical boundaries.
Join us for a practical discussion on how leading organizations are moving beyond AI governance frameworks to build clear accountability models that align AI innovation with security, privacy, compliance, and enterprise risk management.
What You’ll Learn
- Why accountability—not just governance—is becoming the defining challenge of enterprise AI
- Where accountability breaks down across AI vendors, embedded AI capabilities, agents, and complex data flows
- How to establish clear ownership for AI systems, data access, and business outcomes
- What regulators, auditors, boards, and enterprise leaders are beginning to expect
- Practical approaches for enabling AI innovation without sacrificing security, compliance, or control