Least Privilege Non-Human Identities: How to Enforce Access

Key Takeaways

Least privilege non-human identities programs work when permissions follow observed workload behavior, not a human role copied onto a service account.

IBM's X-Force team found that large supply chain and third-party compromises have nearly quadrupled since 2020, as attackers exploit trusted developer identities, CI/CD platforms, and SaaS integrations. That is the machine layer, and the tighter each identity's access, the less a stolen credential can reach.

Service accounts, workloads, and agents need their own owners and identities before access can be reduced safely.

The model holds when ownership, observed use, and current task context drive the access decision, rather than a policy name that looks harmless.

How do you enforce least privilege non-human identities?

Enforce least privilege non-human identities by basing permissions on observed behavior, a declared purpose, and current context, then reducing access in stages you can watch and reverse. The method follows the logic of NIST's Zero Trust Architecture, which treats every access decision as something to evaluate at the moment of the request rather than something inherited from a grant that worked last quarter. Start by giving every non-human identity an owner and a reason to exist, compare what it is granted against what it actually calls, and cut the difference carefully. Least privilege for machines is less a one-time tightening than a practice of keeping access matched to work as that work changes.

• Inventory every NHI and tie it to an owner, a credential, a workload, and a business purpose.
• Compare granted permissions with real use across cloud, SaaS, CI/CD, and Kubernetes.
• Remove unused permissions through staged changes, monitoring, and a ready rollback.
• Recheck access whenever workload behavior, environment, or owner changes.

Quick Facts

What evidence supports a least privilege non-human identities change?

Least privilege changes fail when they rest only on what a policy name appears to allow. For machine identities, evidence has to come from behavior and ownership rather than assumption. Four kinds of proof carry most of the weight.

• Usage evidence shows which permissions were never called during a meaningful window.
• Ownership evidence confirms who can approve, test, or roll back a reduction.
• Blast-radius evidence ranks risky access ahead of low-impact cleanup.
• Runtime evidence shows whether a change caused denied calls, retries, or failed jobs.

Reduce access in small batches and validate each cut against live behavior, the way NIST's SP 800-63B digital identity guidelines treat credential strength as something you verify rather than assume. Evidence also helps teams avoid the common trap of removing only the permissions that are easy to see. The dangerous ones tend to hide in trust policies and delegated paths, not in the obvious list of unused actions.

The observation window decides whether the evidence means anything. A meaningful window covers the full duty cycle of the workload, not the convenient span of a sprint. A nightly batch job needs at least a month of telemetry before an unused permission can honestly be called unused. A quarterly reconciliation service needs a quarter. Cutting on a two-week sample is how a least privilege non-human identities program earns a reputation for breaking things, and reputations like that end programs.

Why is least privilege non-human identities harder than user access review?

Human access is reviewed through managers and job roles on a familiar cadence. Machine access has owners scattered across code, schedules that span from milliseconds to months, and credentials that never take a day off. A person forgets a password. A machine identity uses its key every minute of every day, which is exactly why an over-permissioned one is such a prize.

Capital One remains the clearest illustration. In 2019, an attacker exploited a misconfigured web application firewall and used an over-permissioned role to reach cloud-hosted data for roughly 106 million people. The Office of the Comptroller of the Currency later assessed an 80 million dollar penalty, citing the bank's failure to establish sound risk controls before moving sensitive operations to the cloud. The lesson is blunt: the credential was legitimate, and the damage tracked the permissions behind it. The MITRE ATT&CK Unsecured Credentials technique describes how routinely that credential is simply lying around to be found, in a config file, an environment variable, or a build log.

The pressure is not easing. The 2026 X-Force Threat Intelligence Index reports that large supply chain and third-party compromises have grown nearly fourfold since 2020, as attackers exploit trust relationships and CI/CD automation across development workflows and SaaS integrations. Those are machine credentials. The token taken from a build runner belongs to no one who will notice it missing.

Where does risk move in least privilege non-human identities programs?

Risk moves through automation dependencies. One workflow assumes a role, that role can pass another, and the second role reaches data the first was never trusted with. No single grant looks dangerous under review. The path does. The safe question is not whether a service account holds a permission, but whether the action it is taking right now, against this data, still matches its job. The OWASP Non-Human Identities Top 10 catalogues these failure modes, from long-lived secrets to over-privileged service accounts, precisely because they recur.

The operating model has to separate evidence from action. Discover the service account, workload identity, agent, and credential. Understand what each is used for, who owns it, and what it can reach. Enforce a smaller, context-aware grant at the point of use. This is the machine-layer expression of zero trust: never assume access is fine because it worked yesterday.

Least Privilege Workflow for NHIs

How do you enforce least privilege NHI controls without breaking automation?

The safe path is incremental and evidence-led. Teams that enforce least privilege NHI policies successfully start with the access an identity clearly never uses, watch the workload through a full cycle, and only then approach the permissions tangled in trust policies and delegation. Standards-based authorization helps here. When an identity authenticates through a protocol like OAuth 2.0 with narrowly scoped grants, you can tighten what it receives without rewriting the workload, because the scope lives in the access token rather than in application code.

A staged reduction has four moves, and skipping any one of them is where automation breaks:

• Shadow first. Simulate the proposed policy against recent activity logs and count what would have been denied.
• Batch small. Remove one class of unused action at a time, not an entire rewritten policy.
• Watch the workload. Alert on denied calls, retry storms, and failed jobs for the identity you touched.
• Keep rollback ready. Version the policy so restoring it is one action, not an incident.

When least privilege reaches AI agents, the ground shifts again, because the agent chooses its own next call. An agentic workload cannot be scoped by predicting its behavior in advance. The NIST AI Risk Management Framework offers a governance vocabulary for tying an agent's access to a documented purpose, so the grant is judged against intent rather than against a list of tools the agent happens to hold.

Common pitfalls to avoid

• Copying a human role onto a service account, which grants far more than the workload ever calls.
• Removing only visible unused actions while leaving privilege chains and delegated paths untouched.
• Reviewing NHIs once at creation and never rechecking as the workload, environment, or owner changes.
• Sampling too short a window, then treating a permission the workload uses quarterly as dead access.

Why does dynamic least privilege cloud access matter?

Static grants age badly. A role that is appropriate during a deployment is excessive an hour later, and the difference is standing privilege that nobody is watching. Dynamic least privilege cloud access closes that gap by binding the grant to the task. The identity receives what the current action needs, for as long as the action runs, and nothing after. Short-lived credentials make the pattern practical, because an expired token cannot be replayed at leisure.

The practical test is simple. If an attacker stole this identity's credential at 3 a.m. on a Sunday, what could it do? Under a static grant, everything the role allows, whether or not any work is running. Under dynamic least privilege cloud controls, close to nothing, because no task is in flight and no scope has been issued. That difference is the entire argument, and it does not require blocking a single legitimate call.

How does Token Security approach least privilege non-human identities?

The challenge

Machine identities do not sit in one directory. They live across cloud IAM, Kubernetes, CI/CD, SaaS, secret stores, and AI runtimes, each with its own credential type and naming. A team may know a workload exists without knowing which identity carries its access, who owns it, or what it can reach through an indirect path.

The approach

Token Security treats every service account, workload identity, and agent as a governed non-human identity with an owner, a purpose, and a lifecycle. Discovery builds the inventory. Entitlement mapping shows what each identity can reach. Blast-radius analysis ranks the exposure. Behavioral baselines separate normal use from drift. Its NHI security framework ties discovery, governance, and threat detection into one model, and automated remediation turns a finding into a staged reduction across the non-human identity lifecycle.

The outcome

Teams reduce machine privilege without breaking the automation that depends on it. Access tracks observed behavior, stale credentials get rotated or retired, and privilege chains surface before an attacker finds them. The same control loop covers service accounts, workloads, OAuth apps, and agents, so least privilege becomes a standing practice rather than a project.

How security leaders are applying this model

Least privilege for machines only works when accountability is attached to the identity and the fix can happen fast. Klaviyo connects the two directly.

"Token Security addresses a critical issue faced by companies - the growing threat introduced by non-human identities. Our experience shows that teams need non-human identity accountability, automated remediation, and actionable insights to manage these risks."
Brian Kerr, VP Security and Trust at Klaviyo

That accountability is what makes a reduction possible. The owner knows whether a permission is still earning its place, and automated remediation keeps the change from stalling in a queue. Lemonade points to the other requirement, which is precision. A least privilege program dies under noise, so the finding has to be specific enough to act on.

"Token Security has enhanced our security by providing us accurate and relevant visibility into machine identity risks, something I have yet to see from other vendors. It's the first service I've seen that delivers on the widely-made claim of finding machine identity risk. It provides us with the right amount of detail to mitigate issues without burdening us with tens of rows of useless alerts."
Jonathan Jaffe, CISO at Lemonade

The future of machine-first security

Machine identities already outnumber people in most environments, and the gap widens every time a team ships a new workload or connects a new agent. Least privilege cannot keep pace as a periodic review. It has to run continuously, driven by evidence about what each identity does and enforced where the identity acts.

The organizations that manage this well stop treating least privilege non-human identities as a compliance checkbox and start treating it as an operating property of the system. They inventory relentlessly, reduce on evidence, and keep access matched to purpose as workloads change. That is what machine-first security looks like in practice: access that fits the work, and keeps fitting it.