As organizations accelerate AI adoption, the conversation has shifted from whether to deploy AI to how to deploy it securely, govern it effectively, and operationalize it across the enterprise. Drawing on exclusive Gartner research and proprietary enterprise data not publicly available, Tom Prowledge, VP of Research, will examine how organizations are approaching AI security, where they're struggling, and what separates early leaders from everyone else.

The session will explore enterprise attitudes toward AI security, governance and operational readiness, highlighting the biggest challenges organizations face as they integrate AI into production environments. Tom will share data on AI adoption maturity, governance gaps, board priorities, and why security and compliance remain the primary barriers to scaling AI safely.

Grounded in Gartner's AI TRiSM (AI Trust, Risk and Security Management) framework, this keynote provides attendees with a research-backed view of where the market stands today and the practical governance considerations organizations should address as they operationalize AI securely at scale.

Training autonomous AI agents on sterile, static datasets leaves organizations vulnerable to model drift, fragile logic, and real-world failure—especially as cyber attacks are fueled by the latest AI models like Mythos and Daybreak. In this panel session, leading cybersecurity experts and AI practitioners explore how hyper-synthetic data—production-grade, full-stack telemetry generated in enterprise-mirrored environments—is revolutionizing AI agent development. Discover how security and product leaders leverage hyper-synthetic data to build, train, and stress-test autonomous red, blue, and control agents against dynamic threat landscapes.