Should organisations manage human, machine, and AI identities in separate access queues?

Why This Matters for Security Teams

Separating human, machine, and AI identities into different queues can look operationally tidy, but it often obscures the real control objective: who can reach the same sensitive data, from which path, and with what privilege amplification. That matters because service accounts and AI agents often sit in the same application flows as users, secrets, and APIs. The OWASP Non-Human Identity Top 10 treats mismanaged non-human access as a core risk, not a niche admin issue.

NHIMG research shows how quickly exposed credentials become real-world exposure: in the LLMjacking: How Attackers Hijack AI Using Compromised NHIs analysis, publicly exposed AWS credentials were attempted within an average of 17 minutes. That speed makes queue separation dangerous when it slows visibility or creates inconsistent review paths. A unified exposure model is usually more effective than a split-by-identity-type workflow, because it lets analysts compare risk across people, workloads, and agents using the same criteria. In practice, many security teams discover shared exposure only after a secrets leak or agent abuse has already widened the blast radius.

How It Works in Practice

A better model is to route access requests by risk and business context, not by whether the requester is human, machine, or AI. In a unified queue, reviewers can see the target system, the data sensitivity, the privilege requested, the authentication method, and the identity type in one place. That aligns with NIST Cybersecurity Framework 2.0 expectations around governance, access control, and continuous risk management.

For operational triage, teams usually separate by approval logic, not by intake channel. For example:

• Human users may require manager or system-owner approval for elevated access.
• Machine identities may require workload attestation, short-lived credentials, and scoped secrets.
• AI agents may require task-level authorization, runtime policy checks, and explicit tool boundaries.

That approach also fits NHIMG guidance on lifecycle discipline in the NHI Lifecycle Management Guide and the broader risk patterns described in the Top 10 NHI Issues. A unified queue does not mean every request gets the same reviewer; it means every request is assessed against the same exposure model. That lets security teams automate low-risk renewals, escalate unusual combinations, and detect when a human account, service principal, and AI agent all touch the same data path.

Where this works best is with shared policy-as-code, centralized entitlement visibility, and consistent logging across identity classes. These controls tend to break down in heavily fragmented environments where separate IAM tools, ticketing flows, and ownership models prevent a single team from seeing the full access path.

Common Variations and Edge Cases

Tighter queue separation can reduce operational noise, but it also increases fragmentation, requiring organisations to balance reviewer specialization against a unified view of exposure. That tradeoff is real in regulated environments, where different teams own human IAM, cloud workloads, and agent governance. Current guidance suggests the queue can be separate only if the risk model stays unified underneath it.

There are a few exceptions. Very high-volume machine-to-machine access may justify a dedicated automation lane, but only if the same controls still feed into one exposure ledger. Likewise, AI agent requests may need specialized review for tool use, context windows, or data egress, yet they should not be isolated from the rest of the access picture. The Ultimate Guide to NHIs — Key Challenges and Risks and the Ultimate Guide to NHIs — Regulatory and Audit Perspectives both point to the same underlying issue: auditability fails when identity classes are governed in silos.

Best practice is evolving, especially for agentic workloads, but the direction is clear. If separate queues make it harder to answer “who can access this data right now,” they are adding risk rather than reducing it.

Related resources from NHI Mgmt Group

• How should security teams govern access across human, NHI, and AI identities?
• How should security teams run access reviews for non-human identities?
• How should security teams govern non-human identities that have persistent access?
• Should organisations prioritise machine identities before human access reviews?