AI Agents Are Moving Faster Than Identity Governance. Here’s How to Close the Gap.

An employee asks an AI agent to prepare a customer update. The agent searches a shared drive, checks a CRM record, summarizes a support case and drafts a response. It finishes in seconds.

But which identity did it use to access the files? Did it see more customer information than the task required? If it passed work to another agent, did that second agent inherit the same permissions? And if something went wrong, could anyone trace the action back to a person who was accountable for it?

These are not hypothetical scenarios for organizations using AI agents. They are the identity security questions that surface when software begins choosing its own path through systems and data.

Agents can use tools and credentials, take multiple steps, and adapt their approach to reach a goal. That flexibility creates real opportunities to improve productivity, but it also changes the governance challenge. An agent can act quickly, repeat actions at scale and follow its instructions without the human judgment that helps employees recognize when a request feels inappropriate.

That means identity governance has to account for more than who or what can access a system. It must also consider what an agent is meant to do, what it actually does, and whether it should be allowed to continue. Essentially, authorization is dependent on the agent’s intent matching it’s behavior.

Agents are arriving through various doors

There is no single place where an organization’s agents live. Some are embedded in vendor products. Others are created in low-code tools, built by engineering teams, added as browser extensions or connected through multi-agent workflows. Employees may also turn to public AI services when approved tools don’t meet their needs.

Each route creates a different governance concern. A vendor-embedded assistant may work with the access a user already has. A low-code agent may run with the credentials of the identity who created it. An engineering team may create agents across cloud accounts and code repositories. An unsanctioned browser tool may expose confidential information to an external service.

The details vary, but the underlying questions are familiar: What agents exist? Who is responsible for it? What can it reach? Is it still needed?

Manual tracking struggles to keep pace. Agents can be created and published quickly, while their connections, credentials and ownership can be difficult to see from one place. If an organization can’t establish an inventory, it can’t reliably assess the risk or decide what controls are needed – you cannot govern what you cannot see.

An agent can inherit more than a task

The permissions behind an agent may come from a user, an application identity, a service account or a combination of connected tools. That access can be broader than the task requires.

Consider an agent asked only to read a customer record. If its credentials also allow it to change that record, create folders or access unrelated files, the agent has more authority than its purpose demands. The credential may be valid, but that doesn’t make every action taken with it appropriate.

This is the familiar principle of least privilege applied to a less familiar kind of actor. For people, organizations have developed processes for requesting access, assigning approvals, reviewing permissions and removing access when roles change. Agents need governance too, but the human lifecycle alone can’t manage them. An agent doesn’t join the company, attend security training or notify a manager when its work is done. It can outlive the project that created it.

Delegation makes the picture more complex. One agent may call another, passing work and potentially authority along the way. If each agent carries broad permissions into the next step, it can become difficult to determine which identity ultimately changed a record, what authorized the action or where responsibility lies.

Start with posture: know what you have

Effective governance begins with visibility. Organizations need to discover agents across the platforms where they are created, understand how each one is configured and map the identities, permissions, data and tools connected to it.

A useful agent record should establish at least:

1. Ownership: Who is accountable for the agent and its outcomes?
2. Intent: Why was it created, and what task should it perform?
3. Authority: Which credentials and permissions does it use?
4. Reach: What systems, data and other agents can it access?
5. Activity: What does it do, and has that behavior changed?
6. Status: Is it still needed, or should it be retired?

These details create a baseline against which an organization can evaluate risk. They also make it possible to spot agents that have no clear owner, carry permissions beyond their intended scope or remain active after their original purpose has ended.

An explicit purpose matters especially. If no one has defined what an agent is supposed to do, it becomes much harder to recognize when its behavior has drifted. Clear ownership and intent give governance something concrete to measure.

Posture is a starting point, not the whole answer. A record of permissions can show what an agent could do; it cannot prove that every action it takes is appropriate. For that, governance has to move closer to the moment of action.

Move from posture to runtime authorization

Runtime governance evaluates actions as they happen. Instead of checking access only when an agent is created, it considers each tool call in context: the agent’s identity, owner, intent, requested action, data involved, current risk and previous behavior.

The decision shouldn’t always be a simple allow or block. A low-risk action may proceed. A request involving sensitive data may require more evidence or approval. A suspicious action may be paused, escalated or denied. A clear record should capture what happened and why the decision was made. Runtime checks also need to happen quickly enough to preserve the value of automation. If every routine action waits for a manual review, the agent may no longer save time. But if the system checks only whether a credential is valid, it can miss whether the action itself fits the agent’s purpose. Governance must make careful decisions without adding friction everywhere.

Build the foundation before adding the gate

A practical path to agent governance can develop in stages.

First, find the agents and connected identities across the environment. Then assign accountable owners and document each agent’s purpose. Map its credentials, permissions, tools and access to data. Establish what normal activity looks like and identify changes that could increase risk. From there, apply runtime controls that can evaluate actions and respond in proportion to the risk.

The stages depend on one another. Runtime decisions are only as meaningful as the inventory, ownership and intent behind them. An organization that tries to enforce behavior without understanding what its agents are or why they exist may block useful work, allow activity it doesn’t understand or both.

AI agents don’t make identity governance obsolete. They make its foundations more important and extend the work from periodic reviews to decisions made at machine speed. When organizations can see each agent, understand its authority and connect its actions to a defined purpose, they can support useful automation while keeping accountability in view.