Beyond Identity: The Next Problem in AI Security Is Agent Behavior

Authentication and authorization still matter. Organizations need to know which user is operating an agent and what that user is permitted to access.

However, the user's permissions do not necessarily tell you what the agent will do with them.

A developer may have legitimate access to a repository, but that does not mean every action an autonomous coding agent takes in that repository should automatically be trusted. The agent might attempt to download files it does not need, install an unexpected package, execute a risky command, or make a change that falls outside organizational policy.

That gap between an authorized user and an inappropriate action is what agent security needs to address.

A lot of security tooling is built around telemetry. A sensor observes something happening, then sends the event to a backend for a security team to investigate.

That is useful, but agentic systems introduce a different opportunity.

Instead of waiting for an agent to access a sensitive file and generating an alert afterward, a security control can evaluate the action against policy at the point where the agent attempts it.

That is the approach AgentKeeper takes to inline governance on supported control paths.

MCP has become an important part of the agent ecosystem, and it provides a useful place to establish controls around routed MCP traffic. However, not every action performed by an agent travels through an MCP server.

Coding agents can interact directly with the local filesystem. They can execute commands, make web requests, and invoke application-specific tools. If security only sees protocol traffic, it can miss other parts of the agent's behavior.

AgentKeeper approaches the problem with distributed, application-native integrations that observe supported agent activity where it occurs, rather than relying exclusively on a centralized gateway or network choke point.

The architecture is built around a Go binary and an integration ecosystem for supported applications. The goal is to get close enough to the agent's actions to understand and govern them.

There is an important distinction between detection and AI. Not every security decision needs another AI model.

Organizations should be able to express clear policy decisions such as:

AgentKeeper uses deterministic rules for these types of controls, including built-in rules and custom policy. Where an integration exposes a pre-execution control point, those rules can be evaluated before the action runs.

AI can then help security teams understand intent, correlate events, and investigate suspicious activity. A simple, explicit security policy should not depend on another model making a probabilistic decision.

This becomes particularly important in large, distributed organizations. An enterprise may have thousands of contractors working across multiple countries, using different infrastructure, development environments, and AI tools.

The organization still needs to protect its code and data. Banning AI agents is not realistic, and neither is giving agents unrestricted access.

Governance provides another option. Security teams can define policies around what agents are allowed to access and which actions require intervention. That makes it possible to enable AI while reducing the potential blast radius of an unsafe or compromised agent.

Traditional security often focuses on the identity, the application, the endpoint, and the network. Agent security adds another layer: the behavior of an autonomous system operating through those existing permissions.

The agent becomes an execution layer between a user and the systems the user can access. That execution layer needs security controls of its own.

As organizations deploy more coding agents, productivity agents, and increasingly autonomous enterprise systems, the ability to understand and govern agent behavior will become more important.

The goal is to make AI adoption safe enough to scale without slowing it down.