AI agents create an identity-security problem that traditional access controls were not designed to solve.
Unlike conventional applications,
AI agents are unpredictable, autonomous and outcome-driven. They may dynamically choose among multiple paths to accomplish a task. An agent that is given permissions beyond those it immediately needs can try to act outside the parameters of its intended purpose well before conventional monitoring catches up.
If traditional
identity controls can't secure agent actions, then we must move beyond the question of what an agent can access. Instead, the control should be over what an agent should be doing at a given moment. An access gateway can provide that enforcement layer because it sits between agents and resources and evaluates requests before they are executed.
"An access gateway is essentially sitting in the line of traffic," explains Saviynt Product Management Leader Suresh Sridharan. "[It] monitors the actual request that is going to go down to the target resource and then makes a determination as to whether or not to allow that request to go through."
How runtime authorization evaluates every AI-agent action in context
Traditional
identity and access management systems will typically authenticate an identity and grant it predetermined, static permissions. But that model doesn't work with AI agents because their behavior is nondeterministic and can vary according to context, such as prompts, models, environment and available tools.
Instead of evaluating static permissions, the access gateway applies dynamic runtime authorization, evaluating individual actions as they come.
The gateway can consider which agent is making a request; whether the agent is acting for itself, a human or another agent; to which resources the agent wants access; whether the human requesting the action of the agent has the appropriate privileges; the agent's intended purpose; and current risk factors.
In a
recent blog post, Saviynt Chief Product Officer Vibhuti Sinha describes this change as an identity-first shift from static authorization to constant monitoring and evaluation. With runtime authorization, each API call can be checked before execution — a far cry from trusting an AI agent simply because it previously had been authenticated.
"Only if the action is permitted does the gateway allow access to flow through to enterprise systems," writes Sinha. "This architecture transforms access control from a binary gate (logged in or not) to a continuous evaluation engine."
How real-time policy enforcement can prevent unauthorized actions and data exposure
An AI agent's stated intent provides an important additional signal. As Sridharan explains, an agent assigned to retrieve a single user's Salesforce opportunities might instead try to retrieve every user's opportunities and then filter them to get those belonging to the original user.
The goal may be legitimate, but the intermediate action violates the user's actual intent.
"An agent is basically driven purely by outcomes," Sridharan says. "And that is one of the biggest threats: How do you actually make sure that an agent is not getting access to information that it's not supposed to?"
A policy engine built into the access gateway can evaluate identity, intent, context, risk and anomalies, then allow or deny an action. Policies can also protect information after legitimate access occurs, such as by redacting sensitive data before a response is returned.
In his blog post, Sinha gives an example of how an intent-aware gateway can narrow the scope of permitted actions by letting an agent update customer contact information only during normal business hours. If humans are using and monitoring the same systems, it lessens the risk that an agent's actions might go beyond scope and delete customer data
Meanwhile, Sridharan points out how failure to contain scope could lead to unintended disaster.
"I could say, 'Clean up my folders,'" he says. "Does 'clean up my folders' mean 'delete my folders'? Or does it mean organize my folders? And if the agent is trying to delete folders, then I would say, 'You know what, that doesn't seem to match the intent.'"
How zero standing privileges lessen risk
Standing privileges are especially dangerous things to provide for autonomous software. Saviynt recommends giving each AI agent a verified identity and combining just-in-time access with least privileges.
That would grant the agent only those permissions necessary to perform a certain task, grant them just before the start of the task, and revoke the permissions after the task is completed.
Short-lived, narrowly scoped access also helps prevent privilege propagation when agents call upon other agents or services. Rather than letting downstream agents inherit broad authority, access can remain bound to the specific transaction.
"If an agent's effective privileges deviate from its approved scope — due to integrations, configuration changes, or new tools — the system should automatically flag or restrict the agent for review," Sinha writes.
The gateway can therefore enforce zero standing privileges by making access decisions dynamically according to policy and the nature of each request.
AI-agent security cannot end with authentication. Instead, that's only the beginning.
Organizations need dynamic, continuous
identity governance to establish what the agent is, while runtime controls determine what that agent should be permitted to do at a given moment.
By combining unique agent identities with intent-aware authorization, real-time enforcement and zero standing privileges, an access gateway creates guardrails directly in the execution path, where an autonomous agent's decisions can become real-world actions.
"It is no longer enough to know what an agent can do based on its provisioned permissions," write Sinha. "Organizations must enforce what an agent should be doing based on its intended purpose — and those decisions must be evaluated in real time, action by action."
