Exabeam launches AI agent behavior analytics as part of its latest security operations enhancements, extending behavioral analytics and threat detection to monitor autonomous AI activity across enterprise environments giving security teams visibility into emerging risks created by the rapid adoption of AI agents acting inside corporate systems.

The new capability places AI agent behavior analytics at the heart of how security operations teams detect, investigate and respond to anomalous activities by autonomous agents addressing gaps left by traditional SIEM and XDR tools that were designed primarily for human user activity.

A New Security Challenge Emerges With Autonomous AI Agents

AI agents are quickly becoming embedded across enterprise systems. From automated customer support bots and intelligent data-processing pipelines to autonomous IT workflows and AI-driven business process automation, these agents increasingly interact with sensitive applications, data repositories, and infrastructure components.

While these systems deliver efficiency and scalability, they also introduce new security risks. AI agents can:

• Access systems at machine speed and scale

• Perform actions without direct human supervision

• Interact with multiple data sources simultaneously

• Operate continuously across environments

In many cases, their activity can resemble insider behavior—except that the “insider” is not a human employee but an autonomous system acting on predefined goals, learned behavior, or compromised instructions.

Traditional security tools struggle to distinguish between legitimate AI-driven automation and risky or malicious deviations. This gap creates blind spots where AI agents may unintentionally violate policies, expose sensitive data, or be manipulated by attackers without triggering conventional alerts.

Exabeam’s AI Agent Behavior Analytics is designed specifically to close this gap.

Extending Behavioral Analytics Beyond Human Users

Exabeam is widely known for its User and Entity Behavior Analytics (UEBA) technology, which applies machine learning and statistical modeling to detect anomalous behavior across users, devices, and applications. The new AI agent analytics capability builds on this foundation, extending behavioral modeling to autonomous systems operating within enterprise environments.

Instead of treating AI agents as generic service accounts or background processes, Exabeam models them as distinct behavioral entities with defined roles, expected access patterns, and operational boundaries. This allows the platform to establish a baseline of “normal” agent behavior and identify deviations that may indicate risk.

Examples of suspicious AI agent behavior include:

• Accessing systems or datasets outside its defined purpose

• Performing unusually high volumes of actions in short periods

• Exploring unfamiliar systems or resources

• Moving or copying data in unexpected ways

• Operating at times or from locations inconsistent with its role

By continuously analyzing telemetry from agent interactions, Exabeam can surface early indicators of misuse, compromise, or unintended consequences long before damage escalates.

Addressing Gaps Left by Traditional SIEM and XDR Tools

Most security information and event management (SIEM) and extended detection and response (XDR) platforms were architected around human-centric threat models. They focus on users, endpoints, network traffic, and known attack techniques, often relying on static rules or signature-based detection.

AI agents challenge these assumptions. Their behavior may not align with traditional indicators of compromise, and their actions may appear legitimate at a surface level. For example, an AI agent accessing large volumes of data may be performing its intended function—or it may be leaking sensitive information due to misconfiguration or manipulation.

Exabeam’s behavior-centric approach shifts the focus from static rules to contextual understanding. Instead of asking whether an action matches a known attack pattern, the platform asks whether the behavior makes sense for that specific agent, given its purpose and historical activity.

This shift is critical in an environment where threats increasingly arise from misuse, misalignment, or exploitation of automation rather than from overt malware or intrusion attempts.

Unified Investigation of AI Agent Activity

Detection alone is not enough. Security teams also need efficient ways to investigate and respond to incidents involving AI agents. Exabeam’s new capability unifies AI agent activity into its existing investigation workflows, providing analysts with a timeline-driven view of events.

Within a single interface, analysts can:

• Track an AI agent’s actions over time

• Correlate agent behavior with user activity, system events, and data access

• Understand the context surrounding anomalous behavior

• Assess potential impact and prioritize response actions

This unified investigation model eliminates the need to pivot between disparate tools or manually correlate logs from different systems. It also reduces alert fatigue by providing clearer narratives around what an AI agent did, why it was flagged, and what risk it poses.

By treating AI agents as first-class entities within security operations, Exabeam helps SOC teams adapt their workflows to a future where autonomous systems are deeply integrated into business processes.

Measuring and Improving AI Security Posture

In addition to detection and investigation, Exabeam’s AI agent behavior analytics introduces measurable insights into an organization’s overall AI security posture. Security leaders gain visibility into how effectively AI usage is governed, monitored, and controlled across the enterprise.

The platform provides maturity tracking that helps organizations answer critical questions, such as:

• How many AI agents are active across the environment?

• Which agents access sensitive systems or data?

• How often do agents deviate from expected behavior?

• Are monitoring and controls improving over time?

These insights support continuous improvement, enabling organizations to refine policies, tighten controls, and align AI deployment with risk tolerance and regulatory expectations.

As AI governance becomes a growing concern for boards, regulators, and customers, having measurable indicators of AI security maturity is increasingly important.

Why AI Agent Behavior Analytics Matters Now

The rapid acceleration of AI adoption has outpaced the development of corresponding security controls. Many organizations are deploying AI agents faster than they can fully understand or govern their behavior. This creates a new class of risk that sits somewhere between insider threat, automation failure, and external attack.

Industry experts have warned that AI agents can become high-value targets for adversaries. If compromised, an agent with legitimate access can cause significant harm without triggering traditional security alarms. Even without malicious intent, poorly governed agents can violate compliance requirements, expose sensitive data, or disrupt critical operations.

Exabeam’s expansion into AI agent behavior analytics directly addresses this challenge by applying proven behavioral detection principles to autonomous systems. By doing so, it helps organizations move from reactive monitoring to proactive governance of AI-driven activity.

Supporting Secure AI Adoption at Scale

One of the key goals of Exabeam’s latest release is to enable organizations to confidently scale their use of AI. Rather than slowing innovation through restrictive controls, behavior analytics provides a way to monitor risk dynamically while allowing AI agents to operate as intended.

This balance is critical. Enterprises want to leverage AI to improve productivity, reduce costs, and gain competitive advantage. At the same time, they must maintain trust, security, and compliance across increasingly complex digital environments.

By embedding AI agent analytics into existing security operations, Exabeam allows organizations to extend familiar processes and tools into new domains, reducing friction and accelerating adoption.

The Evolving Role of the Security Operations Center

The launch of AI agent behavior analytics reflects a broader evolution in the role of the Security Operations Center (SOC). Modern SOCs are no longer focused solely on detecting external attackers or compromised endpoints. They are becoming governance hubs for all forms of digital activity, including automation and AI.

As autonomous systems take on more responsibilities, SOC teams must adapt their models, metrics, and skills to account for non-human actors. This includes:

• Understanding how AI agents are designed and deployed

• Collaborating with IT, data, and business teams on governance

• Monitoring behavior rather than just events

• Responding to incidents involving automation failures or misuse

Exabeam’s approach supports this evolution by integrating AI agent oversight into existing SOC workflows rather than treating it as a separate discipline.

Positioning Exabeam in the AI Security Landscape

With the introduction of AI agent behavior analytics, Exabeam is positioning itself at the intersection of security operations, behavioral analytics, and AI governance. While many security vendors are adding AI features to improve detection, Exabeam is addressing the inverse problem: securing AI itself.

This distinction is important. As AI becomes both a tool for defenders and a component of enterprise infrastructure, organizations need solutions that address both sides of the equation. Exabeam’s latest release recognizes that AI agents are not just tools but actors that require oversight, accountability, and control.

Looking Ahead: The Future of Autonomous Threat Detection

As AI continues to reshape enterprise workflows, the number and complexity of autonomous agents will only increase. From self-optimizing infrastructure to AI-driven decision systems, these agents will operate at speeds and scales that challenge traditional security models.

Tools that can monitor, model, and respond to agent-driven activity will become essential components of modern security architectures. Behavioral analytics, contextual investigation, and posture management are likely to play central roles in this new paradigm.

Exabeam’s launch of AI agent behavior analytics signals an important shift in security operations—one that treats autonomous digital actors as first-class entities alongside human users. By extending behavioral detection into this new domain, Exabeam is helping organizations prepare for a future where trust, visibility, and control must extend beyond people to the intelligent systems that increasingly act on their behalf.

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