Frontier AI models are changing the security landscape at machine speed. They discover vulnerabilities far faster and in greater volume than before, surface zero-day flaws in major systems, and chain lower-severity bugs into exploitable attack paths. The gap between disclosure and exploit is shrinking to hours. Yet AI alone can't fix what it finds — independent research shows autonomous patching without expert review does more harm than good.

In this webinar, two experts break down both sides: what these AI-driven threats mean, and how to defend against them. You'll learn a practical framework spanning platform foundations, patching at scale, hardening and virtual patching, lateral security with microsegmentation, and recovery — so your organization is ready before an incident, not during one.

Top 5 reasons to attend:

1. Understand how frontier AI models are reshaping the threat landscape, from zero-day discovery to automated exploit chaining, and why this higher velocity is the new baseline.
2. Learn why autonomous AI patching is net-negative without expert review, and what the right balance between AI acceleration and human judgment looks like.
3. Explore a practical, multi-layered defense framework spanning foundations, patching, hardening, lateral security, and recovery.
4. See how hardening and virtual patching at the infrastructure and workload layers help intercept exploit attempts between vulnerability disclosure and patch application.
5. Walk away with a recovery strategy that goes beyond immutable backups to include isolated recovery environments, behavioral validation, and orchestration runbooks built before an incident, not during one.