AI Agent Security Starts with Identity, Attribution, and Least Privilege

There is a recurring pattern in enterprise AI agent deployments: security teams often reach for gateways first, even though gateways are among the controls they are least prepared to operate effectively. The reason is straightforward. Gateways depend on reliable identity, attribution, and delegation data—capabilities that many organizations have not yet established for AI agents.

The first layer of risk is not theoretical. In June, CISA added a LiteLLM vulnerability to its Known Exploited Vulnerabilities catalog after attackers were observed exploiting it in the wild. The vulnerability was chained with a second flaw that enabled command execution on the host through the gateway itself without requiring credentials. This was one of seven common vulnerabilities and exposures (CVEs) disclosed in that AI gateway during a single month.

For many organizations, the gateway is the first security layer they deploy to protect AI agents. That is the wrong starting point. In a secure agent architecture, gateway enforcement should be the fifth control—not the first.

Most AI agent security maturity models describe the controls enterprises will eventually need. In practice, however, they often overlook the more difficult question facing organizations with existing systems: in what order should these controls be implemented alongside established identity and access management (IAM) platforms?

The control plane cannot make informed decisions if it does not know which agents are running, who delegated their work, what tasks they are performing, and which credentials they are using. Gateways can block obvious policy violations, but without this context, they cannot reliably distinguish legitimate activity from actions that are technically permitted but operationally inappropriate.

When security controls are introduced sequentially into a production AI agent environment, the failure pattern is clear. Enforcement is implemented early, while the identity and attribute context required to support it remains incomplete. AI agent security operates as a dependency chain: each control depends on context generated by the controls upstream.

The Wrong Starting Point for AI Agent Security

Consider routing agent traffic through a new runtime gateway. A Financial Reconciliation Agent attempts to modify a record in a production environment. The gateway authenticates the user token and evaluates the API call. What it may not know is that the request was initiated by an AI agent, that the agent has a narrowly defined responsibility, or that the request is part of a tool chain triggered by an untrusted artifact.

The credentials are valid, and the API call is permitted. However, the action violates the purpose of the delegation. The gateway exists, but the supporting identity and attribution controls do not. As a result, expensive enforcement is being applied to only a small part of the overall risk picture.

Limiting an agent to the privileges of the human principal it serves can help prevent privilege escalation. However, a privilege cap does not provide independent attribution. Even if 20 agents operate under one authority, each agent still requires a unique identity, audit trail, operational profile, and revocation path.

Dependency-Gated AI Agent Deployment

I call this approach dependency-gated deployment. Upstream control requirements must be met before downstream security controls can be considered operationally complete. Downstream controls can be developed in parallel, but they should not be treated as fully effective until their dependencies are in place.

Below are six security gates and the evidence required to demonstrate that each one is working.

Six dependency gates for AI agent security. Each control is informed by the gates above it, based on the author’s analysis of production agent deployments.

Start with an Agent You Can Actually Name

Begin by identifying the agents operating across open-source frameworks, cloud platforms, SaaS applications, and developer tools. For every agent, document its owner, business purpose, lifecycle stage, approved tools, data domains, and credential sources.

Skipping this step can cost an organization valuable time during incident response. Security teams may spend the first hours of an investigation determining which agents exist and what they are supposed to do. A complete inventory establishes the assets that all subsequent security controls must manage.

Give Agents Their Own Identity Without Losing Human Accountability

Do not embed an AI agent inside a developer token, shared service account, or human session. Simply knowing that the caller is an agent is not enough. The control plane also needs delegation context: who assigned the work, which task the agent was authorized to perform, and which resources it needs to access.

The agent identity identifies the actor making the call. Delegation context explains under whose authority the action is being taken and why. Both are necessary for reliable attribution.

If this connection is lost, downstream logs may attribute the coordinating agent’s actions to the employee whose token was borrowed. That creates an inaccurate audit trail and assigns responsibility to someone who did not initiate or directly control the agent activity.

Reduce Privileges Before Monitoring Behavior

Once an agent has been identified, limit what it can do. Access should be restricted by task, time, tool, resource, and data domain. These controls can often be implemented through existing IAM capabilities, including workload identities, token exchange, conditional access, and time-limited entitlements.

According to Teleport’s 2026 research, which surveyed 205 security leaders, organizations’ access scope often exceeds their ability or confidence to predict AI-related incidents. Organizations reporting over-privileged AI systems reported an incident rate of 76%, compared with 17% among organizations using least-privilege controls.

The findings reinforce an important point: access scope is a foundational part of the AI agent security dependency chain. It is more important to establish appropriate permissions early than to rely solely on context-aware runtime enforcement later.

The central principle is monotonic delegation. Every transfer of responsibility must preserve or reduce authority; it must never increase authority. For example, an agent responsible for reviewing one ledger should receive access to that ledger—not the employee’s unrestricted access to every system the employee can use.

Fix Attribution Before Automating Runtime Enforcement

Most audit systems can record which resources were accessed and which credentials enabled access. However, agent deployments frequently fail to connect those events to the specific agent, task, and human principal involved.

Before implementing adaptive runtime policies, connect relevant tool calls to the agent ID, initiating principal, task ID, parent action, and result. Then test the telemetry. For a completed task, verify that you can determine who started it, which agent performed it, which privileges were used, which tools were called, and what outcomes followed.

In regulated environments, activity without clear attribution is difficult to defend. Security teams need a complete and reliable chain of evidence from delegation through execution.

Once Context Exists, the Gateway Can Do Its Job

With registered identities, explicit delegation, scoped credentials, and detailed telemetry, a gateway can make meaningful authorization decisions. It can determine whether a specific agent is authorized to perform a particular action, during a particular task, against a particular resource.

A user’s credentials may technically provide write access to the Financial Reconciliation Agent. However, the gateway can now recognize that the requested write operation falls outside the agent’s assigned scope. This is where gateway security delivers its greatest value.

The strictest runtime controls should be applied at irreversible boundaries, including payments, access-policy changes, data deletion, production modifications, and sensitive data exports.

Place Detection and Kill Paths at the End of the Chain

Behavioral baselines should be developed after agent identity, delegation, access control, and telemetry are established. Creating reliable baselines requires a clear understanding of normal and attributable agent activity.

Once that foundation exists, security teams can identify unusual tool usage, unexpected cross-domain access, and deviations from assigned tasks. Containment must involve more than disabling a single directory object. Effective termination paths may include disabling the agent identity, revoking active and derived credentials, blocking tool activation, terminating active tasks, and isolating the workload hosting the agent.

Improve AI Agent Security Without Replacing IAM

Organizations do not need to create an entirely new identity program to secure AI agents. If the existing identity provider does not support agents as a native object type, begin with an authoritative agent registry linked to existing workload identities.

Next, treat agent and task identifiers as trusted execution context. Use short-lived credentials to reduce inherited privileges, and include agent, principal, and task identifiers in tool invocation logs so they can be consumed by downstream gateways and security systems.

As vendor support improves, the dependency model remains the same. According to Okta’s 2026 survey, only 34% of executives said their organizations consistently apply the same level of security rigor to AI agents and human employees.

This gap cannot be solved by applying the final control in the chain first. Effective agent security requires the identity, attribution, access, and telemetry foundations that make downstream enforcement possible.

What to Do in the Next 30 Days

Start with 10 production agents. For each agent, identify the owner, purpose, approved tools, data access, lifecycle status, and credential sources. This exercise will provide an initial view of your agent inventory and governance gaps.

Test attribution. Confirm that your IAM systems and logs can distinguish each agent from the person or service that delegated its task. If they cannot, the gateway will operate without the context required for accurate enforcement.

Reconstruct one completed task. Follow an agent task from initiation through every tool call and downstream effect. Any point where the action chain becomes unclear represents a security and accountability gap.

Implementing downstream enforcement before the required identity and attribution context exists can create a false sense of security. Maturity models describe the destination. Dependency-gated deployment provides the build order needed to reach that destination without weakening production security along the way.

Nik Kale is a principal engineer specializing in enterprise AI platforms and security.

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