Our recent blog post on Mythos and identity security established why identity security is the foundation of any enterprise security program in the age of AI. This post goes deeper on one specific gap that most organizations have not yet addressed: the AI agents already operating inside enterprise environments, and whether they are operating under the right controls.
AI agents are identities first, applications second. They hold credentials, initiate sessions and execute actions across systems at a speed no human team can monitor. Most do not come with their own access. They inherit it from a user's credentials or a shared service account carrying far more privilege than any single task requires.
According to a 2026 Cloud Security Alliance study, 74% of organizations say AI agents often receive more access than necessary.
The instinct is to treat this as an access provisioning problem. It is not. The issue is how much access an agent receives at the start and what happens to that access once the session begins.
How do AI agents change the equation?
Most security programs treat access as a moment rather than a process. A credential is checked, a session opens and the assumption is that the environment is secure. That assumption holds when the identity on the other end is a human making deliberate decisions within a defined scope. It does not hold for an agent that inherits access, operates autonomously and has no human in the loop to catch what it should not be doing.
Mythos demonstrated this at scale. An AI system capable of autonomously developing working exploits for vulnerabilities that survived decades of human review operates much like a compromised agent or human agent inside your environment. It has speed, autonomy and inherited access with no one watching. The Cloud Security Alliance rated AI agents as a critical attack surface for exactly this reason.
Most agents do not arrive with a defined set of permissions
They operate under a user's credentials or a shared service account, inheriting whatever access that account carries. That access was not designed for the agent. It was designed for a broader purpose, accumulated over time, and rarely reviewed with the agent's actual task in mind.
The result is an agent with standing access to privileged data it has no business touching, operating under permissions nobody explicitly granted it. Access controls were not built to catch that.
Understanding why requires distinguishing between two terms that are often used interchangeably but should not be: authentication and authorization.
Authentication is not authorization
Authentication confirms who an agent is at the moment it connects. That is where its role ends. Authorization governs what that agent is permitted to do within the session. Without it, an authenticated agent operates with whatever standing access it inherited, unchecked, for as long as the session runs. That is the exposure.
Consider a common authorization vs. authentication example. An agent is deployed to query a database. It authenticates successfully using an inherited service account. That account also has write access to a financial system the agent was never meant to touch. Authentication completed without issue. Nothing in that process asked whether the agent should have access to the financial system. Nothing prevented it from using that access. The agent was authenticated. It was never authorized.
This is not an edge case. It is the default condition for most AI agent deployments. Agents inherit access that was never designed for them, and without runtime authorization in place, that access goes unchecked for as long as the session runs.
Runtime authorization is the answer
Runtime authorization means that every action the agent takes is continuously evaluated against identity, resource and the context of the request. Access is scoped to what the task requires. Every privileged action is evaluated the moment it happens, and the policy is set by humans. Enforcement happens automatically on every action the agent takes, without requiring human review at execution.
The distinction matters because agents are non-deterministic. Unlike a scripted process that executes the same steps in the same order, an agent's behavior changes based on context and instructions. An action that was appropriate in one session may not be appropriate in the next. Static access reviews cannot keep pace with that variability. Runtime authorization can.
The session is where control happens
Knowing an agent exists and understanding its risk are necessary starting points. Neither stops an agent from acting on access it should not have. Control happens at the session level, and it has to be continuous.
Most organizations can tell you an agent connected. Few can tell you what it did after that. Did it stay within expected boundaries? When did it begin doing something it was never meant to do? That gap is where runtime enforcement matters most.
Every action an agent takes inside a session is evaluated against the policy governing it. When an action falls outside that policy, the response is immediate. There won’t be a flag for review or an alert in a queue. The action is stopped before it is completed.
Session recording extends that control into accountability. A complete record of every privileged action an agent took inside a session does more than satisfy an auditor. It tells security teams exactly what the agent did, in what order, and whether it stayed within the boundaries it was given. When something goes wrong, the answer is already there.
Runtime authorization controls what an agent does. Session recording proves it.
What accountability actually looks like
Security leaders are being asked to answer for what their AI agents do. It's a reasonable expectation that most organizations cannot currently meet.
Accountability requires more than a policy document and an access review
It requires evidence: a complete, traceable record of every privileged action an agent took, tied back to the human who authorized the policy that governed it. Every session is monitored. Every action is on the record before anyone asks for it.
Regulatory pressure around AI accountability is accelerating. Audit capability for AI-initiated activity is moving from best practice to legal requirement across multiple frameworks, and the pace of that shift is not slowing down. Organizations building that capability now are not reacting to a regulation. They are building the posture their boards will ask about well before any deadline arrives.
AI agents are not going away. The question is whether the security program governing them was built for the identity class they represent.