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Modernizing the CERT Insider Threat Framework for the Agentic Enterprise
White Paper
A Framework to Detect Insider Risk Across Humans, Agents, and Automated Systems
This white paper explains how to extend CERT insider threat principles to detect insider risk in environments that include AI agents and non-human identities.
Insider risk is expanding as organizations adopt autonomous systems, machine identities, and AI-driven workflows. Traditional detection methods focus on isolated events and known indicators, creating gaps when trusted activity appears legitimate but accumulates risk over time.
This white paper shows how to modernize insider threat detection using CERT principles and Behavior Intelligence. It helps you connect behavioral patterns, cross-system context, and identity relationships to identify risk earlier and prioritize investigations more effectively.
Key Questions This White Paper Helps You Answer
- How has insider risk expanded beyond human users to include AI agents and machine identities?
- Why do traditional detection models miss risk that appears legitimate in isolation?
- What behavioral signals indicate insider risk across users, agents, and systems?
- How can CERT principles be applied to environments with autonomous workflows?
- What does Behavior Intelligence reveal that event-based detection cannot?
How Exabeam Helps You Detect Insider Risk
Exabeam New-Scale Fusion applies user and entity behavior analytics (UEBA) and Agent Behavior Analytics (ABA) to monitor activity across human and non-human identities. It correlates activity across systems and identifies behavioral progression that traditional detections miss.
Exabeam Nova accelerates investigations by assembling context, prioritizing risk, and guiding response workflows, helping your team detect insider risk earlier and reduce investigation time.
Download the white paper to modernize insider threat detection and identify insider risk across users, agents, and systems before it leads to impact.
