Rethinking identity for AI agents and NHIs in Zero Trust

At a glance

What this is: This is an analysis of how identity for AI agents and other non-human identities shifts from human MFA to machine-centric verification.

Why it matters: It matters because IAM, PAM, and lifecycle teams must govern non-interactive access, short-lived credentials, and continuous evidence across both machine and human identities.

👉 Read Unosecur's analysis of identity controls for AI agents and NHIs

Context

AI agent identity is different from human identity because there is no user to prompt, approve, or re-authenticate in the middle of execution. In practice, that means access control has to rely on policy, attestation, and short-lived credentials that can be governed at runtime.

The governance gap is not authentication alone. For AI agents, workloads, service accounts, and bots, the real issue is whether identity lifecycle, logging, ownership, and de-provisioning are built into the access model before machine actions start to accumulate risk.

Key questions

Q: How should security teams govern AI agent identities in Zero Trust environments?

A: Security teams should govern AI agent identities with machine-native authentication, short-lived credentials, runtime policy, and continuous logging. The key is to treat the agent like a non-human identity that must be scoped, attested, and de-provisioned, not like a person using MFA. Zero Trust only works here when every request is evaluated against current context.

Q: Why do service accounts with standing privilege increase security risk?

A: Standing privilege increases risk because a compromised service account can be reused for later movement, broader access, and long-lived abuse. When credentials do not expire with the task, the attack window stays open far longer than necessary. Short-lived access, strong ownership, and automated revocation reduce that exposure materially.

Q: What breaks when human MFA is used for bots and AI agents?

A: Human MFA breaks machine workflows because bots cannot complete interactive approval steps in a reliable or secure way. It also fails to prove software provenance, which is the real trust question for non-human identities. The better control is policy-based authentication tied to workload identity, attestation, and request context.

Q: How do organisations know whether machine identity controls are working?

A: They know controls are working when every non-human identity has an owner, an expiry, a logged action trail, and no persistent secrets in code or configuration. If tokens survive long after the task, or if access cannot be tied to a workload and purpose, the governance model is not operating as intended.

Technical breakdown

Why human MFA fails for AI agent identity

Human MFA assumes an interactive person who can respond to a challenge. AI agents and other NHIs authenticate through non-interactive flows such as OAuth client credentials, workload identity federation, or certificate-backed access, so MFA adds friction without proving software provenance. The control problem shifts from proving presence to proving policy compliance, scope, and runtime trust. In Zero Trust terms, the identity decision must happen per request, not only at login.

Practical implication: replace human challenge flows with policy-driven machine authentication and verify the identity of the workload, not just the token.

Short-lived credentials and federation reduce standing privilege

Static secrets are durable attack assets. Federation, STS, managed identities, and similar short-lived credential patterns reduce how long a compromised credential remains useful and remove much of the rotation burden that grows around stored keys. This does not eliminate risk, but it changes it from persistent exposure to bounded exposure. For machine identities, the key governance question is whether any credential outlives the task it was issued for.

Practical implication: remove long-lived secrets from code and infrastructure, and make expiry the default for machine access.

Why attestation and SPIFFE change machine trust

Attestation adds proof about where a workload is running and what it is running as, while mTLS binds that identity to service-to-service communication. SPIFFE and SPIRE formalise this pattern by issuing short-lived workload identities that can be rotated automatically and tied to runtime signals. That creates a stronger trust chain than token possession alone, because the system can distinguish an actual workload from a copied credential. For AI agents, this is the difference between owning access and proving legitimate execution context.

Practical implication: bind authorization to attested workload identity and runtime context, not just to possession of a secret.

NHI Mgmt Group analysis

Human MFA is the wrong control model for AI agent identities: MFA was designed for interactive users, not for non-interactive execution by software actors. AI agents and NHIs authenticate through machine-centric flows, so the security question becomes whether the identity can be trusted, scoped, and revoked without human prompts. The implication is that IAM programmes must stop treating bot access as a human login problem.

Short-lived credentials expose the real NHI governance gap: Static secrets create standing access that outlives the task, the deployment, and often the owner. Federation and managed identity patterns narrow that exposure window, but only if lifecycle controls are actually enforced. The implication is that credential duration, not just credential strength, is the control variable practitioners need to manage.

Software identity must be proven, not assumed: Attestation, mTLS, and SPIFFE-style workload identity shift trust from token possession to runtime proof. That matters because AI agents and workloads can hold valid credentials while operating in the wrong environment or under the wrong orchestration state. The implication is that identity governance now has to account for execution context as well as entitlement.

Lifecycle governance is the common discipline across human, machine, and agent identities: Ownership, least privilege, logging, rotation, and de-provisioning are not separate programmes for different actor types. The difference is in how they are applied at runtime, not in whether they are needed. The implication is that identity teams should manage AI agents, service accounts, and users through one governance model with actor-specific controls.

Runtime identity governance is the named concept this topic exposes: identity is no longer a static entitlement set but a live evidence stream that must be validated throughout execution. Once AI agents and workloads act without human pacing, access review, rotation, and de-provisioning all depend on runtime signals rather than periodic checkpoints. The implication is that mature programmes will measure trust continuously, not retrospectively.

From our research:

• 91.6% of secrets remain valid five days after the targeted organisation is notified, showing a critical gap in remediation procedures, according to Ultimate Guide to NHIs.
• Only 5.7% of organisations have full visibility into their service accounts, which means most identity teams cannot reliably inventory machine access.
• For a broader view of lifecycle and rotation controls, see Ultimate Guide to NHIs for the governance patterns that close persistent access gaps.

What this signals

Runtime identity governance is becoming the dividing line between programmes that can manage machine access and those that only document it. When non-human identities act at machine speed, periodic reviews are not enough, because the risk lives in the execution window, not just in the entitlement record.

The operational signal to watch is whether your programme can tie every workload, agent, and service account to owner, purpose, expiry, and evidence. If it cannot, then lifecycle controls are still paper controls, even when the authentication layer looks modern.

With 97% of NHIs carrying excessive privileges in our research, the problem is not simply the presence of machine identities but the mismatch between how they are provisioned and how they actually behave. That is why Zero Trust for NHIs has to connect attestation, short-lived credentials, and de-provisioning into one control loop.

For practitioners

• Replace human MFA flows for machine identities Use app-only authentication, workload identity federation, or certificate-backed access for non-interactive actors. Keep human prompts out of machine execution paths and scope every token to the task it supports.
• Eliminate long-lived secrets from code and infrastructure Move service credentials into federation or managed identity patterns, then enforce expiry by design. This reduces the blast radius of leakage and removes the assumption that a secret can be safely reused across tasks.
• Bind authorization to attested workload identity Require attestation and mTLS where workloads need to prove runtime context, then tie policy decisions to namespace, image digest, node, or environment metadata. Token possession alone should not be enough for sensitive service access.
• Make lifecycle controls mandatory for every NHI Assign an owner, define purpose, set expiry, log actions, and automate de-provisioning when the workload or project ends. Without ownership and offboarding, machine identities become persistent access paths with no accountable operator.

Key takeaways

• AI agents and other NHIs need machine-native identity controls because human MFA does not fit non-interactive access.
• Short-lived credentials, attestation, and lifecycle governance matter because standing privilege and persistent secrets keep the attack window open.
• The real programme test is whether every non-human identity is owned, scoped, logged, and de-provisioned as part of one continuous control model.

Key terms

• Non-Human Identity: A non-human identity is a machine or software actor that authenticates to systems and receives access like an account. It includes service accounts, API keys, tokens, certificates, workloads, bots, and AI agents. The governance challenge is that these identities often move faster and scale wider than human accounts.
• Workload Identity: Workload identity is the cryptographic identity assigned to software running in a specific environment. It lets systems verify what the workload is and where it is running before granting access. For modern identity programmes, this is the bridge between access control and runtime proof.
• Attestation: Attestation is evidence that a workload, device, or runtime environment is what it claims to be. It strengthens trust by linking identity to execution context, such as image, node, namespace, or platform state. In machine identity governance, attestation reduces the chance that copied credentials become usable anywhere.
• Standing Privilege: Standing privilege is access that remains available without needing fresh justification or re-approval for each use. In machine identity environments, it creates a persistent attack surface because compromised credentials can be reused long after the original need has passed. Reducing standing privilege is central to Zero Trust.

What's in the full article

Unosecur's full blog covers the operational detail this post intentionally leaves for the source:

• Step-by-step examples of app-only authentication and workload identity flows for non-interactive access
• Implementation detail on federation, managed identities, and temporary credential patterns
• Practical guidance on attestation and SPIFFE/SPIRE for workload identity assurance
• Lifecycle control examples for ownership, logging, rotation, and de-provisioning

👉 The full Unosecur post covers workload identity, attestation, and lifecycle governance in more detail

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are responsible for identity security strategy or NHI governance in your organisation, it is worth exploring.