For years, security teams have built their systems on the simple premise that if you control identity, you can control risk. Employees authenticate with identity providers. Service accounts connect systems. API keys allow workloads to talk to cloud services and databases.
The characters have become very predictable. And as a result, the security model of ownership and governance followed that prediction. Now, this situation is breaking.
AI agents have quietly entered the business, summarizing meetings, writing emails, helping employees find information. Most security teams didn’t think too hard about them at first. They looked like production tools, because that’s exactly what they were.
Then, organizations started connecting them to key business services like Salesforce, Snowflake, GitHub, Jira, production databases, and cloud environments. Now, they receive information, trigger workflows, review records, write and execute code, and take action across multiple systems.
Sometimes in a person’s name, sometimes independently, and sometimes in ways that are not entirely clear.
This makes AI agents more than just tools. It makes them an identity and most businesses don’t have their security and governance models in place.
The pattern is consistent across organizations. The new identity layer is built on top of the existing infrastructure with virtually no one to control the identity groups that have spent the last decade creating it. An agent may be created by one team, deployed by another, connected to five different applications, and using credentials that were provided for a completely different purpose.
It got wide access early on because someone needed it to work and didn’t want to slow things down. The result is an accumulation of high-privileged actors, who are barely visible to most watchdog groups, let alone control them.
AI agents create, deploy, and rotate identities at machine speed, bypassing traditional IAM controls.
Token Security helps teams manage the complete lifecycle of AI agent ownership, reduce risk through maintenance, and maintain governance and test readiness without sacrificing speed.
Request a Tech Demo
According to the 2026 CSA survey commissioned by us here at Token Security, 82% of organizations have experienced at least one AI agent created without the knowledge of security, IT, or management teams in the past year, and 41% have experienced this happening multiple times.
This is where the security discussion goes sideways. Much of the attention to AI security has gone to the risk of the model, such as rapid injection, jailbreak, unsafe results. While these are all important parts of the AI ecosystem, they don’t paint the complete picture that enterprise security teams need. The most important piece they need to answer is what exactly can the agent access?
An agent that summarizes public documents has a limited area of exposure. An agent connected to customer records, source code, financial systems, and management-level cloud credentials is a completely different issue.
Bad information, a compromised session, a malicious plugin, or a poorly configured integration can turn an overly privileged agent into a means of data exfiltration, malicious action, or collective movement through systems it was never intended to connect to.
This is no longer a theory, 65% of organizations have experienced a security incident involving an AI agent in the past year, with 61% reporting disclosure or mishandling of sensitive data as a result (source).
Gaining control starts with visibility. Security teams need AI agent discovery and a vocabulary that goes beyond words and platforms to answer really important questions.
Who owns this agent? Who can you ask for? What systems is it connected to? What guarantees does it use? What can each target application read, write, delete, or use?
This is harder than it sounds, because the surface is invisible. The security team may know that the sales assistant is in the AI environment without knowing that she is using a Snowflake service account with administrative privileges. They may know that a code agent is installed on developer endpoints without knowing what secrets, repositories, and CI/CD pipelines it can access.
The agent itself is only part of the picture. Everything that can affect an agent’s identity is a real point of exposure.
The second episode is objective. Security and management cannot be based on consent through AI agents. It must respond to the agent’s intent. A sales preparation agent only needs read access to CRM records. It does not need to delete database tables.
A financial workflow agent should only read invoices. It should not be able to create new lucky users. If you understand what the agent is supposed to do, you can check if its permissions match that scope. And, in practice today, they rarely do and that gap is where the real danger resides and widens over time with the drift of minority rights policy.
Once the goal is understood, enforcement takes place. Permissions can be trimmed to match the agent’s original purpose, overprivileged service accounts are fixed, unused information is rotated or deleted, and dangerous communications are caught before they become incidents.
The part that affects most teams is that none of this is a one-time job. An access review or assessment may sound like progress, but it only provides a time-check box and a false sense of security. The reason is that agents change, instructions are updated, user bases change, and integration grows.
An agent that started out as a small internal tool can end up silently connecting to systems it wasn’t designed to touch, not because anyone made a bad decision, but because no one was watching when the scope went in.
This is why governance must continue to catch agents who begin accessing applications outside of their normal pattern, use unexpected information, or perform actions inconsistent with their stated purpose.
Businesses that succeed with AI won’t be the ones that block agents entirely. They will be the ones that make the agents manageable and promote the innovation of secure AI. This means treating them as first-class properties with ownership, access, behavior, risk, and lifecycle controls.
AI agents become privileged insiders. Security and identity systems must now take action before those insiders become invisible attack methods.
We’d love to show you how we approach this at Token Security, book a demo to chat with our technical team so you can scale without sacrificing security.
Powered and written by Token Security.
