Securing the Agentic Enterprise — Agent Behavior Analytics, OWASP Top 10, and the AI Insider Threat
Exabeam April 2026: enterprises are struggling to baseline normal AI agent behavior, investigate potential misuse, and detect emerging agentic insider threats. ChannelInsider: Exabeam extended Agent Behavior Analytics to ChatGPT, Copilot, and Gemini, giving enterprises visibility into AI agent activity across the major AI platforms. Business Wire: Exabeam monitors agent behavior against the OWASP Top 10 for Agentic AI — prompt manipulation, excessive privileges, insecure tool usage, and model misuse.
The problem is structural. Traditional security tools were not built for AI agents that act on behalf of users. A human user with access to your CRM looks like a person. An AI agent with access to your CRM looks like an API. Traditional UEBA does not know what normal AI agent behavior looks like because the category is genuinely new.
Why AI Agents Create a New Threat Surface
AI agents differ from human users in ways that matter for security.
Machine speed: an AI agent can make thousands of decisions per day. A human makes dozens. The volume and velocity of agent actions creates a threat surface that human-focused security tools are not designed to monitor.
Credential proliferation: one user authorizes one agent. The agent then acts with that user's full access rights. When that user delegates to an agent, the agent inherits all the user's permissions without additional scrutiny.
Prompt manipulation: an AI agent can be manipulated through inputs in ways that do not look like traditional credential compromise. An attacker embeds malicious instructions in data the agent processes. The agent follows instructions that look like legitimate commands. The security system sees valid credentials and plausible instructions. It does not see the attack.
Autonomy: the agent acts without the user watching every action. A human user reviewing their own activity can notice anomalies. An agent running autonomously for hours between check-ins can cause significant damage before anyone notices.
The agentic insider threat is where this becomes most serious. Traditional insider threat is a human employee misusing their access. Agentic insider threat is an AI agent misusing its access — either because it was manipulated or because it was given excessive privileges in the first place. Exabeam: AI agents push these limits even further. The agent has real privileges and can exfiltrate data, escalate access, or take actions the human user would not have authorized.
The OWASP Agentic Top 10 — The Threat Framework
Business Wire: Exabeam monitors agent behavior against the OWASP Top 10 for Agentic AI. The framework provides a structured threat taxonomy that security teams can audit against.
The four threat categories most relevant to enterprise deployments:
Prompt manipulation: an attacker embeds malicious instructions in data the agent processes — an email, a document, a database entry. The agent interprets these instructions as legitimate commands. The system sees valid credentials and plausible instructions. The attack succeeds because the agent was manipulated, not because credentials were stolen.
Excessive privileges: the agent was given more access than it needs. Misuse of that access goes undetected because the agent is operating within its granted permissions. The security system sees authorized access. It does not see that the access was unnecessary and therefore risky.
Insecure tool usage: the agent calls tools in ways that expose data or create vulnerabilities. The agent has a legitimate function. It uses that function in a way that creates a security hole. The tool call looks normal. The consequence does not.
Model misuse: the agent is used for purposes it was not designed for. This is both an external threat — an attacker using the agent for unintended goals — and an internal governance failure.
What each threat looks like in practice: prompt injection could be an agent reading a poisoned email and hallucinating that it should forward all customer records to an external address. Privilege escalation could be an agent with CRM read access using that access to export contact data it was never authorized to export. Data exfiltration could be an agent with email access sending sensitive attachments to an unauthorized recipient. Tool abuse could be an agent's tool-calling capability being exploited to run arbitrary code.
What Agent Behavior Analytics Actually Does
Exabeam: Agent Behavior Analytics applies behavioral modeling to human users and the AI agents acting on their behalf. Just as UEBA established what normal looks like for human users, ABA establishes what normal looks like for AI agents. Deviations from normal behavior trigger alerts regardless of whether the agent has valid credentials.
What ABA detects that traditional tools do not: anomalous data access patterns when an agent accesses data it does not normally access, unusual API call volumes when an agent suddenly makes thousands of calls when it normally makes dozens, out-of-character actions when an agent attempts operations it has never attempted before, and cross-tenant data movement when an agent moves data between data stores it should not be bridging.
The session-based analytics approach: Exabeam detects risky AI agent behavior with session-based analytics and first-time activity insights. ABA tracks the full session of an agent — what it did, in what sequence, with what context. First-time activities are flagged for review. An agent that suddenly accesses a new data source for the first time triggers an alert.
The baseline problem is the hardest part. ChannelInsider: enterprises are struggling to baseline normal AI behavior. ABA solves this: you cannot detect anomalies without knowing what normal looks like. Building the baseline requires observing agent behavior over time, which means ABA deployment is not instant. It requires a learning period before it becomes effective.
Why ChatGPT, Copilot, and Gemini Are the Starting Point
ChannelInsider: Exabeam extended ABA to ChatGPT, Copilot, and Gemini, enabling visibility and anomaly detection for enterprise AI agent activity across all three major platforms.
The enterprise AI reality: most enterprises have deployed or are deploying ChatGPT through OpenAI, Microsoft Copilot across the Microsoft 365 suite, and Google Gemini within Google Workspace. Each of these has agents acting on behalf of users within the enterprise. Each generates activity logs that traditional security tools do not understand.
What Exabeam's extension covers: visibility, anomaly detection, and security for enterprise AI agent activity across all three platforms. Enterprises can now have unified behavioral visibility regardless of which AI platform their agents run on.
Why this matters for security teams: without ABA coverage across these platforms, security teams have no visibility into what AI agents are doing in their environment. With it, security teams can detect when an AI agent — regardless of platform — starts behaving abnormally.
The AI Agent Security Stack — What Enterprises Need
A five-layer framework for AI agent security:
Layer 1 — Identity and Access Management: which agents have access to which systems, what is the principle of least privilege for agents, which humans authorized which agent actions, and which agent actions require human authorization.
Layer 2 — Agent Behavior Analytics: what does normal agent behavior look like, when is the agent acting outside its baseline, which first-time activities should be flagged, and how does session-based analytics detect anomalous agent behavior.
Layer 3 — OWASP Agentic Top 10 Threat Intelligence: are agents being targeted by prompt injection, are agents attempting privilege escalation, are agents accessing data outside their authorization, and how does monitoring against the OWASP Top 10 provide measurable coverage of these threats.
Layer 4 — Audit Logging and Forensics: what did each agent do, when, and with what context, who authorized each agent action, and what data did each agent access.
Layer 5 — Governance and Policy: what agents are allowed to do, what data are agents allowed to access, and what happens when an agent behaves anomalously.
The CISO action items in order: conduct an agent inventory — most enterprises do not know how many AI agents are operating in their environment. Establish behavioral baselines — what does normal agent behavior look like. Deploy ABA — implement behavioral monitoring for agents. Align with OWASP Top 10 — audit against the threat categories. Integrate with existing SOC — agent security events should flow into the security operations center alongside other security alerts.
If your security team does not know what normal AI agent behavior looks like in your environment, you do not have AI agent security. Start with the agent inventory.