Zscaler Deception: Counter AI Attacks without the False Positives

AI driven attacks are reshaping the kill chain, reducing friction for adversaries and allowing reconnaissance, code generation and lateral movement to happen at machine speed. This session examines how AI agents increase attacker effectiveness while exposing the weaknesses in traditional detection approaches. It looks at the growing challenge defenders face when signatures fail to catch polymorphic code and behavioral tools generate excessive noise from low and slow activity that resembles legitimate administration.

The discussion focuses on closing this detection gap through high fidelity deception strategies that turn attacker exploration into reliable signals. Topics include how AI automates pivoting, why time to pivot is becoming a critical metric and how to deploy decoys, honeytokens and breadcrumbs to eliminate false positives. The session also covers methods for protecting AI infrastructure itself, including LLMs, pipelines and agents, by placing targeted tripwires to identify sophisticated threats early.