AI agents need their own behavioral baselines
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Enterprise internet security vendors and experts have spent many years trying to understand human behavior.
As a result, there is now a wide variety of very effective tools and processes that help distinguish legitimate user activity from behavior that may indicate a compromised account or malicious activity.
Behavioral analytics has come a very long way.
The underlying principle is that behavior is often a stronger indicator of compromise than the use of credentials alone.
In this context, behavioral analytics establishes a strong baseline for individual users over time, including the systems they access, typical login patterns, data usage, administrative actions, API activity and various other interactions.
Any significant deviations can trigger investigation.
But as we all know, things are changing very fast. The rapid move from GenAI assistants to autonomous AI agents has introduced a new kind of enterprise actor, one that combines non-human identity with autonomy, dynamic decision-making and the ability to execute multi-step actions across systems. Existing security models were not designed with this combination of characteristics in mind.
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Clearly, AI agents are now of particular concern thanks to their ability to execute tasks autonomously, access multiple applications, retrieve information, make decisions within defined parameters and complete multi-step workflows.
To say the adoption of AI agents across the enterprise space is dramatic is to put it very mildly. Gartner predicts that by the end of 2026, 40% of enterprise applications will incorporate task-specific AI agents, compared with fewer than 5% in 2025. In order to work, many of these agents will be given identities, permissions, credentials and access to sensitive business systems. It stands to reason that security issues will follow.
Unlike human users, however, most organizations have little understanding of what constitutes expected or abnormal behavior for autonomous identities. That helps explain why Gartner's 2026 Hype Cycle views Agentic AI Security as an emerging discipline, and why governance and behavioral monitoring capabilities are still in their early stages of development.
It also creates a potentially serious disconnect organizations have mature behavioral intelligence for people but comparatively little for AI agents, despite both increasingly operating as trusted identities within enterprise environments.
But is the difference between human and AI behavior really that important? In the pre-AI era, human behavioral analytics relied on relatively stable patterns. Most users worked predictable hours, accessed a consistent set of applications, connected from familiar locations and performed activities aligned with their role. After all, humans are creatures of habit, including in the workplace environment where the vast majority would never do anything malicious.