Artificial intelligence is no longer just a tool for defenders. It’s a decisive force multiplier for attackers. A new adversary class is emerging: the AI.Attacker. Autonomous agents capable of reconnaissance, planning, and execution at machine speed. Unlike traditional threats, these systems adapt, self-learn, and scale campaigns beyond human operational limits. What once took days or weeks in the intrusion kill chain: recon → resource development → delivery → exploitation, can now collapse into minutes.
Yet this speed also exposes their Achilles’ heel: the pre-attack phase. Before execution, AI.Attackers require accurate, timely intelligence to map targets, identify vulnerabilities, and prepare infrastructure. This is the best, and often only, moment to disrupt them.
This talk presents real-world examples of AI-driven recon, resource development, and exploitation, then introduces a live demo of “AI.Attacker Recon and Exploitation Prevention” a technique and tooling suite that detects and prevent hostile AI reconnaissance. Attendees will leave with a forward-shifted defense model and practical tactics to prevent AI reconnaissance, tipping the balance before the first shot is fired.