Organizations are deploying copilots, AI agents, and LLM-driven workflows into production faster than governance, identity architecture, and security operations can adapt. The problem is no longer simply “model risk.” Non-deterministic AI introduces a new attack surface across prompts, agents, data pipelines, toolchains, and runtime behavior. AI is also acting as both a defensive force multiplier and an adversarial accelerant. Static guardrails, prompt filters, and policy controls remain necessary, but they are not sufficient. They do not reliably detect novel behavior, unsafe tool use, goal drift, or risk that only becomes visible when an AI system interacts with identities, data, and downstream systems in context.

This webinar presents a defense-in-depth model for securing AI, built around the realities of runtime behavior. Using the NIST’s Secure / Defend / Thwart framing, it will show how to combine an identity control plane for agents, least privilege, and just-in-time access, secure-by-default configurations, runtime observability, behavior-based detection, anomaly detection, autonomous investigation, and autonomous containment. It will also explain why organizations should treat AI as both a new attack surface and an operational system that must be continuously monitored and contained when it behaves outside expected bounds.

A practical blueprint for a minimum viable AI security baseline will be presented for a sharper understanding of where foundational controls end, and a clear view of why behavioral oversight is becoming the decisive layer for securing AI at scale without slowing innovation.