Enterprise AI needs a new model for behavioral intelligence

Enterprise security vendors and experts have long sought to decipher human behavior, leading to the development of various effective tools and processes. Behavioral analytics has become a crucial aspect of identifying compromised accounts or malicious activity, establishing a strong baseline for individual users based on their typical interactions such as accessed systems, login patterns, data usage, administrative actions, and API activity.

However, the rapid evolution of AI agents, with their non-human identity, autonomy, dynamic decision-making, and ability to execute multi-step actions across systems, has introduced new challenges for security models. These AI agents, which are set to be adopted by 40% of enterprise applications by the end of 2026, require organizations to establish behavioral baselines that differ significantly from those used for human users.

Unlike humans, AI agents may operate continuously, interact with multiple services and APIs, and make independent decisions, creating a new layer of abstraction between user requests and performed actions. As AI agents gain more permissions and access to sensitive systems, organizations must adapt their security processes to monitor and manage these agents effectively.

Monitoring AI agents requires understanding their expected behavior, context, and the potential consequences of their actions. By establishing and maintaining behavioral baselines for AI agents, organizations can ensure their security processes evolve in tandem with these new AI-driven identities.