How should teams handle users who have access that does not match their peers?

Why This Matters for Security Teams

Peer variance is rarely a harmless exception. In identity programmes, users who carry access that does not match their peers often signal a broken joiner-mover-leaver process, an outdated role model, or access that was granted for a one-off exception and never removed. The practical risk is not only excess privilege, but also hidden business logic that bypasses normal approvals and review paths. That is why current guidance from the OWASP Non-Human Identity Top 10 and NIST control practice around least privilege both point toward continuous validation rather than periodic paperwork. The same pattern appears in NHIMG research: the Ultimate Guide to NHIs reports that 97% of NHIs carry excessive privileges, which is a strong reminder that over-assignment is common, not exceptional. For security teams, peer variance should be treated as a signal to prove need, not as an administrative note to carry forward. In practice, many security teams encounter toxic access only after an incident review reveals that “temporary” exceptions had become normal operating state.

Teams often get stuck because access reviews are built to confirm membership, not challenge outliers. A peer comparison approach changes that by asking whether the user’s access is explainable in the context of function, location, product line, support tier, or on-call duty. If the answer is no, the smallest unexplained differences should be routed into validation or removal workflows immediately. Where the control environment is mature, this is paired with evidence from NIST SP 800-53 Rev 5 Security and Privacy Controls, especially least privilege and account review expectations, so the decision is not subjective.

• Compare a user to a meaningful peer group, not the whole department.
• Separate approved exceptions from accidental access drift.
• Require a business owner to validate any outlier entitlement.
• Remove the access when no current need can be demonstrated.

NHIMG’s Ultimate Guide to NHIs — Key Challenges and Risks is useful here because it frames excessive privileges as a lifecycle problem, not a one-time review issue. The same logic applies to human users: if the access shape does not match the job, the reviewer should treat that mismatch as evidence to investigate, not as a reason to defer action. These controls tend to break down when peer definitions are too broad or when application owners refuse to own exception decisions, because then every outlier looks justifiable.

How It Works in Practice

The most reliable process starts with peer grouping. Teams define a comparison set using attributes that actually predict access need, such as job family, region, manager chain, application role, and operational duty. Then the current entitlement set is compared against the peer baseline to identify the smallest unexplained gaps. This is where the workflow becomes operational, not theoretical: one extra admin group, one unused API token, or one access path that no similar user has can all be flagged for review.

From there, the decision path should be simple. If the access is legitimate, document the reason and attach an expiration date or next review point. If it is not, remove it. If there is uncertainty, route it to the entitlement owner or application owner for evidence-based validation. For sensitive systems, teams increasingly apply the same logic used in NHI governance, where the question is whether the identity should have the access at all, not whether it has historically been tolerated. That is aligned with the 52 NHI Breaches Analysis, which shows how missed governance gaps accumulate into real compromise paths.

Operationally, this works best when paired with:

• role and attribute data that is current enough to be trusted;
• an entitlement catalog with clear owners;
• policy thresholds that distinguish expected variation from true outliers;
• automation that can open tickets, revoke access, or trigger approval workflows.

The right metric is not how many reviews were completed, but how much unexplained variance was eliminated. In mature programmes, the process becomes continuous: access diffs are evaluated as soon as a user changes role, joins a new team, or gains a new application relationship. These controls tend to break down in highly matrixed organisations with shared service roles and inconsistent job codes because the peer baseline becomes too noisy to support confident removal decisions.

Common Variations and Edge Cases

Tighter peer review often increases operational overhead, requiring organisations to balance cleaner entitlements against slower handling of legitimate exceptions. That tradeoff is real, especially in regulated environments or teams with frequent project-based access changes. Best practice is evolving, but current guidance suggests that the answer is not to relax the comparison model. It is to make the exception path clearer and time-bound.

Some users legitimately do not match peers. Senior administrators, incident responders, auditors, and launch teams often carry temporary or cross-functional access that will look unusual in any baseline. Those cases should be labelled as exceptions with an owner, purpose, and expiry date. Temporary elevation should be especially controlled, since “approved variance” can quietly become standing privilege if the expiry is ignored. NHIMG’s Ultimate Guide to NHIs is a useful reminder that excess privilege is often sustained by weak lifecycle hygiene, not by malicious intent.

Another edge case is automation-adjacent access. Users who operate scripts, service accounts, or delegated workflows may inherit entitlements that appear excessive when viewed through a normal peer lens. In those cases, the team should separate the human user from the workload identity and review each identity type under the right model. There is no universal standard for this yet, but the safest practice is to preserve a narrow human baseline and document any machine-initiated permissions separately. Teams that cannot do that well usually end up over-granting “just in case,” which is exactly the pattern peer variance is meant to uncover.

Related resources from NHI Mgmt Group

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• How should security teams govern API keys used for generative AI access?