An AI agent rarely works alone. It may call an API, query a database, read from cloud storage, trigger a workflow, or pass a task to another agent. Most protected enterprise systems require the agent or its underlying workload to present an identity and operate under defined permissions. Those identities often receive less scrutiny than employee accounts. That is the governance gap enterprises need to close.
What Are Non-Human Identities?
Non-human identities are like digital ID cards for software, bots, AI agents, and automated processes rather than people. They allow a system to identify and authenticate a workload when it accesses data, calls an API, or communicates with another service. Depending on the system, the identity may present credentials such as an API key, OAuth token, certificate, or service account secret.
A credential authenticates an agent or workload, while authorization policies determine what that identity can do. These controls are distinct, but they must be governed together. A well-protected credential is still dangerous if the associated identity can access every customer record, production system, or administrative function.
AI makes the problem harder because agents can act across multiple systems within a single task. They may also operate on behalf of a user, making it difficult to determine whether an action belongs to the agent, the user, or the underlying service account.
Why Do AI Systems Create a Different Identity Risk?
Traditional applications typically follow predictable request paths. An AI agent can select tools and actions at runtime based on its instructions and the available context.
Consider a support agent that can read tickets, search customer records, and issue refunds. A prompt manipulation attack might cause it to misuse those legitimate permissions for an unintended purpose. Authentication succeeds, but the action remains unsafe.
Agent governance therefore requires more than validating the credential presented at authentication. Authorization, delegation, and audit controls should also establish:
- Which agent or workflow made the request
- Who authorized the task
- What the agent was expected to do
- Which tools and data were required
- Where the agent was running
This context helps distinguish valid automation from an authenticated action that has moved beyond its intended scope.
How Should Enterprises Build an NHI Inventory?
The first step in effective NHI security is discovery. Businesses should take stock of all identities across their cloud platforms, SaaS integrations, CI/CD pipelines, AI tools, agent frameworks, and internal apps.
Every record should include an owner, purpose, environment, authentication method, permissions, dependencies, and last-used time. An API key labeled production-key-2 tells a reviewer almost nothing. A record stating that the key belongs to the invoice agent, accesses a single billing endpoint, and is owned by the finance platform team is far more useful.
Machine identity management should also cover identities created outside formal provisioning workflows. Development teams may generate temporary tokens for testing, and employees may authorize third-party AI applications via OAuth. These connections can remain active long after the original experiment ends.
Which Controls Matter Most?
Enterprises should consistently apply a limited set of controls across the entire AI stack. Use separate identities for different agents and environments. Using the same credentials creates shared accountability and increases the difficulty of safely revoking them. Operate within the constraints of least-privilege access.
Prefer short-lived, workload-bound credentials when the platform supports them. Long-lived secrets increase the window of opportunity for an attacker. The OWASP Secrets Management Cheat Sheet also recommends planning credential rotation as part of the secret lifecycle.
Service account security means conducting ownership reviews, reviewing permissions, and automatically removing service accounts when a workload is retired. API keys should be stored securely, have limited scope, be rotated regularly, have usage monitored, and be quickly revoked. Do not hardcode raw key values in source code, prompts, configuration files, logs, or persistent agent memory. Configuration should, if possible, refer to a secure secret store.
Is Access Control Enough?
No. A correctly issued token can still be used in unexpected ways. Enterprises need runtime visibility into agent actions, data access, tool calls, and policy decisions. As Pluto’s guidance on AI agent access control explains, risk often becomes apparent during execution, not at the time the token is created.
Monitoring should flag unusual destinations, privilege changes, unexpected data volumes, dormant identities becoming active, and actions outside the agent’s assigned purpose. Logs should link the identity, agent, initiating user, requested task, and resulting action without recording raw secrets.
Final Thoughts
Governance should treat non-human identities as active components of the AI system, not as background credentials. Start with inventory and ownership, then enforce narrow permissions, short credential lifetimes, lifecycle controls, and runtime monitoring.
The goal is not merely to confirm that an agent can authenticate. It is to verify that the right agent is performing the right action, with the right authority, for a known business purpose.