Exabeam Agent Behavior Analytics
Type: full-code · Vendor: Exabeam · Status: active · Status in practice: emerging
Exabeam Agent Behavior Analytics establishes per-agent behavioral baselines and surfaces anomalies in real time, triaging high-risk detections above low-fidelity alerts so reviewers keep attending to an agent's findings instead of being buried in noise.
Description. Exabeam extends its UEBA behavioral analytics to AI agents, treating them as non-human insiders with broad access. It establishes normal agent activity, surfaces anomalies tied to misuse or policy violations, and separates high-risk detections from low-fidelity alerts to reduce noise.
Agent loop shape. Sits as an out-of-band analytics layer consuming agent activity, baselining behavior and surfacing prioritized anomalies for human investigation rather than enforcing inline.
Primary use cases
- Behavioral anomaly detection on AI-agent activity
- Triaging agent detections to cut alert noise
- Investigating agent misuse with session timelines
Key concepts
- UEBA for AI agents → trajectory-anomaly-monitor — User-and-entity behavior analytics applied to agents treated as non-human insiders.
- Session narrative → decision-log — Related agent activity connected into one investigable timeline.
- Alert triage — Scoring that separates high-risk detections from low-fidelity alerts to cut noise.
Patterns this full-code implements —
Anti-patterns avoided
References
Provenance
- Last analyzed:
- Last updated:
- Verification status: needs-verification