• Home
>
• Resources
>
• White Paper

Agent Behavior Analytics: Securing the Autonomous Enterprise

White Paper

Understanding how to detect, monitor, and investigate AI agent activity across modern security operations

Autonomous agents introduce visibility and attribution gaps that traditional security models cannot detect.

AI adoption is accelerating, and agents now execute workflows, invoke APIs, and interact with systems using inherited credentials. Traditional identity-based detection cannot track this activity, leaving security teams with limited visibility into agent-driven risk.

This white paper explains how Exabeam extends user and entity behavior analytics (UEBA) with Agent Behavior Analytics (ABA) to detect and investigate agent activity. It introduces a three-tier model for agents and shows how behavioral analytics and chain-of-custody reconstruction improve detection and attribution.

Key Questions This White Paper Helps You Answer

• How do AI agents create new visibility and attribution gaps?
• What threats are specific to agent behavior, such as prompt injection and guardrail violations?
• How does ABA extend UEBA for agent detection?
• Why does credential fragmentation make agent activity difficult to trace?
• How can chain-of-custody reconstruction link user, agent, and system activity?
• What detection strategies apply across different types of agents?

How Exabeam Addresses Agent-Driven Security Risk

Exabeam combines UEBA with ABA to detect and investigate activity from cloud LLM platforms, task agents, and digital workers. Telemetry from AI platforms, identity systems, and cloud logs is normalized into the Common Information Model and correlated to reconstruct execution paths. Behavioral analytics identifies deviations in access, tool usage, and workflows, while Exabeam Nova supports investigation with timeline reconstruction and evidence-based summaries.

Download the white paper to understand how Agent Behavior Analytics helps detect and investigate AI agent activity.