Detecting Offensive Cyber Agents: A Detection-in-Depth Approach
Matt Mittelsteadt, Jam Kraprayoon, Robin Staes-Polet, Oskar Galeev, Jan Wehner, Christopher Covino, Shaun Ee

TL;DR
This paper introduces a strategic detection-in-depth framework and five actionable mechanisms to identify and counter offensive AI-driven cyber agents, addressing a growing detection gap in cybersecurity.
Contribution
It presents a novel strategic framework and five specific detection mechanisms to enhance cybersecurity defenses against autonomous offensive cyber agents.
Findings
Proposes five detection mechanisms for offensive cyber agents.
Defines a detection-in-depth strategic framework.
Introduces the Agentic Cybersecurity Exchange (ACE).
Abstract
Artificial Intelligence (AI) agents can now orchestrate cyberattacks. This development is already increasing the speed and scale of cyber attacks, decreasing attack costs, and improving the operational autonomy of cyber capabilities. To defend against these emerging threats, actors must first develop the capability to detect them. This report frames the offensive cyber agent detection challenge by outlining the coming detection gap between offensive cyber agents and traditional cyber capabilities; introducing detection-in-depth, a strategic framework to guide policymakers and defenders responding to this detection gap; and presents five actionable detection mechanisms to support policymakers, industry, and defenders when putting this strategic framework into practice. These include (1) Agent Identifiers for Critical Infrastructure,(2) Agent Honeypots; (3) AI-Automated Alert Analysis and…
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