Agentic AI Security: Visibility and Control for AI Agents at Work

Security teams have spent years tracking what employees do with data. The harder problem now is tracking what agents do on their behalf.

AI agents, whether running in an IDE, installed locally on a laptop, or connected to internal data through a model context protocol (MCP) server, operate with the permissions of the user who deployed them. They read files, query databases, call external APIs, and generate outputs. And in most enterprise environments, security teams have no reliable way to see any of it.

What Is Agentic AI Security?

Agentic AI security is the practice of detecting, monitoring, and governing AI agents that operate on behalf of employees within an enterprise environment. Unlike traditional AI security that focuses on individual prompts sent to a model, agentic AI security addresses multi-step workflows where agents take autonomous actions, invoke tools, access data sources, and chain together with other agents.

The scope includes local agents installed on endpoints, IDE-embedded coding assistants, MCP-connected agents tied to corporate data sources, and any automated AI workflow initiated by a user but executed without continuous human oversight.

Why Agentic AI Creates a Different Category of Risk

The risks associated with employees using AI tools like ChatGPT or Claude in a browser are well understood at this point. An employee pastes a sensitive document into a prompt. That is a discrete event with a clear actor and a clear action.

Agents change this picture in several important ways, and they do so by crossing a threshold that most security tools were never designed to handle: agents are not users. They are privileged insiders, reasoning and executing on behalf of users and enterprises, often with broader access than any individual human would need.

Agents inherit user permissions

When an employee installs a local AI agent and connects it to Google Drive, that agent indexes everything the employee can access. It does not ask for a subset of permissions. It inherits the full access scope. The same is true for agents connected to production databases, code repositories, customer records, or any other data source with OAuth or API access.

This is not a configuration flaw in most cases. It is how these tools are designed to work. The agent needs broad access to be useful. The security problem is that this access is now exercised programmatically, at scale, without the friction of a human making individual decisions.

Agent activity is not visible to existing tools

Endpoint detection and response (EDR) tools are built to detect malicious executables and process anomalies. Secure access service edge (SASE) tools inspect network traffic. Neither was designed to parse the semantic content of an AI conversation, understand what a tool call means in context, or correlate a sequence of agent actions across multiple steps into a single risk assessment.

An agent manipulated through prompt injection, for example, does not reveal the attack in any single API call. The signal is distributed across an entire conversation thread and a chain of tool invocations. That requires a different kind of visibility than either EDR or SASE provides.

This is the gap that security leaders are asking about most directly: DLP was built for humans. It was built to catch a person copying a file, sending an email, or pasting data into a browser. It was not built to track an agent autonomously reading, writing, and transmitting data across dozens of tool calls in a single session. The same applies to IRM. Insider risk programs were designed around human behavior patterns. Agent behavior does not follow those patterns, and it does not leave the same signals.

Shadow agents create an inventory problem

CISOs frequently know about the AI tools their IT teams formally approved. They do not know about the local agent installed on individual machines, the custom MCP servers a team spun up to automate a deployment pipeline, or the unsanctioned AI connectors that appeared after an off-site hackathon. Consider a developer connecting their enterprise Claude Code account to an unsecured personal Google Drive, or an employee connecting an unvetted third-party agent to their corporate Slack account. These small actions create real data exposure, and most existing tooling will not catch them.

Shadow agents are invisible in the same way shadow IT was invisible a decade ago, except they can act autonomously and at speed.

The Three Pillars of Cyberhaven's Agentic AI Security

Cyberhaven's AI Security addresses agentic risk across three integrated layers: visibility, observability, and controls. The current release focuses primarily on visibility and observability, with control capabilities expanding in subsequent phases.

Discovery: Know what agents are running

Discovery in agentic AI security starts with a continuous, automatically maintained inventory of every AI agent, GenAI application, and MCP server across your environment.

Cyberhaven detects these at the endpoint, inside IDEs, in SaaS, and across MCP-connected systems, including agents that were never formally approved or deployed by IT. Every application and agent is assigned a Risk IQ score across five dimensions. Personal versus corporate usage is detected at runtime, not inferred from network traffic after the fact.

This inventory is not a one-time scan. It updates continuously as new agents appear, as existing agents connect to new data sources, and as usage patterns change. Cyberhaven can detect sanctioned versus unsanctioned applications and usage, as well as usage within personal or corporate accounts, allowing for full visibility to drive better security decisions.

Observability: Reconstruct what agents did

Knowing an agent exists is a starting point. Understanding what it did is where security teams can actually assess and respond to risk.

Cyberhaven reconstructs the full execution lifecycle of every agent interaction: the data accessed, the tool calls invoked, the actions taken, and the complete multi-turn conversation context. This functions as a flight recorder for AI agent activity, capturing what happened before and after any event that surfaces as a concern.

This matters because agentic risk is sequential. An agent that read a sensitive file, passed its contents to a second agent, and then wrote output to an external endpoint did not commit one action. It committed a chain of actions, and the risk only becomes clear when you can see the whole chain.

Cyberhaven correlates that full conversation and execution thread without requiring security teams to manually review individual interactions. Violations emerge from the pattern, not just the individual event.

Controls: Stop high-risk actions before they cause damage

The third pillar is runtime policy enforcement. Guardrails can be configured to block, warn, or redact at the prompt and response level.

Rather than generic block pages, users receive plain-English explanations of what the risk is and why a particular action was stopped or flagged. When a prompt contains sensitive data, users are warned and given the option to revise or proceed. The goal is governance that preserves productivity while creating meaningful friction at the moments that matter.

Controls are intentionally graduated. Blanket blocking generates alert fatigue and pushes employees toward personal accounts. Contextual guidance, delivered at the point of risk, changes behavior without creating adversarial dynamics between security policy and the people it's meant to protect.

Learn more about Cyberhaven's Agentic AI Security capabilities by watching the Spring 2026 Product Launch.

How Cyberhaven AI Security Differs From Other Approaches

The distinction that ties visibility, observability, and controls together is data lineage.

Other agentic AI security tools can tell you what an agent did. Cyberhaven tells you where that data came from, what it contained, and where it went next. That is the difference between an alert and an investigation.

This is possible because Cyberhaven AI Security is built on a data lineage foundation that has already been mapping data movement across endpoints, browsers, SaaS, and cloud environments. When an agent accesses a file, Cyberhaven knows the provenance of that file, its sensitivity classification, and every other system it has touched. When that agent then writes output elsewhere, the lineage thread extends.

This context is not available to tools that approach the problem from the network layer or from a standalone AI monitoring product. It requires endpoint-level instrumentation combined with a persistent data lineage graph, which is exactly the architecture Cyberhaven has built.

The endpoint and browser extension combination also provides an advantage over network proxies. Being closer to the user means capturing events that never traverse a monitored network path, including local agent activity and browser-based interactions with AI tools that use encrypted connections.

Explore the power of AI security with "Governing the Autonomous Enterprise: A Security Framework for Agentic AI."