Traditional identity and access management (IAM) was largely designed around human lifecycle events. An employee joins an organization, receives access, changes roles and eventually leaves. Access can be reviewed against a known individual, a defined job function and an accountable manager.
AI agents are beginning to challenge those assumptions.
Modern enterprises already rely on non-human identities (NHIs), including API credentials, service accounts, workloads, integration accounts and automated processes. AI agents add a new dimension: They can interpret inputs, select actions, invoke downstream systems and execute multi-step workflows without a human approving every transaction.
That changes the identity question. It is no longer sufficient to ask: “Who has access?”
Security and audit leaders increasingly need to ask: “Which non-human identity can act, on whose behalf, with what authority, and within what boundaries?”
A MACHINE Framework for NHI Governance
A practical way to approach this challenge is through seven core controls:
- M: Map non-human identities. Create and maintain a clear inventory of service accounts, API credentials, workloads, integrations and AI agents. An identity that cannot be identified cannot be effectively governed.
- A: Attribute ownership and purpose. Every NHI should have an accountable owner, a documented business purpose and a defined operational scope. “System account” should not be an excuse for unclear accountability.
- C: Constrain privileges and delegation. Apply least privilege to machine identities just as you would to human users. Where agents can delegate access or invoke other identities, those relationships should also be controlled.
- H: Have expirations for credentials and permissions. Reduce reliance on permanent credentials. Short-lived tokens, automated rotation and time-bound permissions can reduce the impact of credential compromise.
- I: Inspect behavior continuously. Authentication alone does not tell the whole story. Monitor what an NHI actually does: which systems it accesses, which APIs it invokes, how frequently it acts and whether its behavior matches its intended purpose.
- N: Notice anomalous actions. Establish behavioral expectations and identify activity outside those boundaries. An AI agent suddenly accessing a sensitive dataset or initiating an unexpected sequence of privileged actions should trigger scrutiny.
- E: Enforce rapid revocation. Organizations need effective circuit breakers. If an NHI behaves unexpectedly or is compromised, security teams should be able to revoke credentials, terminate sessions or isolate the identity quickly.
From Access Verification to Action Authorization
Consider an AI-powered customer service agent with permission to read customer records and update a CRM system. The agent may be correctly authenticated. Its credentials may be valid. Its activity may even appear legitimate from a traditional IAM perspective.
But what happens if prompt injection, compromised context or flawed agent logic causes it to export a large volume of customer data or modify account information outside its intended purpose?
The authentication control has not necessarily failed. The problem is that authorization was defined too broadly.
This is where AI changes the governance challenge. An organization may know which identity performed an action, yet still lack adequate controls over whether that identity should have been permitted to perform that specific action under those circumstances.
For auditors and security leaders, this means extending IAM assurance beyond identity verification toward continuous authorization and action-level governance.
The objective is not to eliminate autonomous systems. It is to ensure that autonomy operates within clearly defined and enforceable boundaries.
As AI agents become more capable, the question organizations should be asking is no longer simply whether a machine identity is authenticated.
It is whether the organization can answer, at any moment: What is this identity allowed to do, why is it allowed to do it, who is accountable for it and how quickly can we stop it?
Q&A Recap
Why are non-human identities becoming a bigger governance issue?
Cloud services, APIs, automation and AI agents increasingly perform actions without direct human execution. This expands the number and importance of identities that organizations must govern.
Why isn’t traditional IAM enough?
Traditional IAM often focuses on identity, authentication and relatively static permissions. Autonomous systems require greater attention to delegated authority, behavioral context and the actions an identity can perform.
What does the MACHINE framework address?
It provides seven practical controls: Map, Attribute, Constrain, Have expiry, Inspect, Notice and Enforce.
What is changing with AI agents?
The governance question is moving from simply verifying who or what is accessing a system to caontinuously determining whether a particular action is authorized within its current context.
What should auditors ask?
They should be able to establish what each important NHI can do, why it can do it, who owns it, whether its permissions expire how its behavior is monitored and how quickly it can be stopped.
About the author: Ali Nouman is a cybersecurity leader with 18 years of experience across retail, enterprise, fintech and highly regulated environments. His work focuses on cybersecurity strategy, AI governance, risk transformation, security operations and translating complex technology risks into measurable operational controls. He has led cybersecurity initiatives across distributed and cloud environments, with a particular interest in the practical application of AI security and responsible AI governance.
