Security teams want to spot unusual AI agent behavior while they’re still defining normal

Security teams are focusing on AI agents as a priority in their behavioral monitoring investments, according to Abnormal AI's 2026 State of Behavioral Security Report. This emphasis is due to the complexity of establishing and maintaining baselines for normal AI agent behavior, which is critical for detecting anomalies. Nearly half of the surveyed security professionals believe their organizations struggle with this task, though behavioral anomaly detection remains a key strategy.

Behavioral anomaly detection is a method used by organizations to identify unusual activities by comparing them against established patterns. This approach is not limited to human actions; it extends to non-human identities like AI agents, service accounts, and API integrations. These entities each have distinctive activity patterns, and deviations can signal potential security incidents. However, defining what constitutes 'normal' behavior for AI agents is proving challenging for many organizations.

The report reveals that while 81% of organizations rely extensively on behavioral anomaly detection, only about 72% feel better equipped to detect unusual behavior from non-human identities compared to human users. This disparity highlights the difficulty in defining and monitoring AI agent activities, which often involve complex interactions across systems. Security teams must understand the specific tasks assigned to AI agents, the permissions used, and how these actions align with their intended purpose.

One challenge is the transient nature of AI agent activities, which can complicate auditing efforts. A security chief from a software company noted the difficulty in tracing short-lived AI activities, such as those lasting only 30 seconds on a virtual machine. Even with visible network traffic, it can be hard to identify the responsible agent, particularly if it was created for a single task or operates within another process.

False positives and the difficulty in defining normal activity are significant obstacles to expanding behavioral baselining, according to respondents. More than half reported that investigating and remediating a behavioral anomaly typically takes at least four hours. This process involves not only detecting a change but also gathering sufficient information to understand and explain it, such as whether an AI agent had a legitimate reason to perform a particular action.