Why do LLM gateways create an identity governance problem for IAM teams?

Why This Matters for Security Teams

LLM gateways do not just proxy traffic. They often become the control point for prompts, tool calls, retrieval, and downstream actions, which means they sit directly in the identity path for both humans and autonomous agents. When IAM is reduced to API keys or coarse service accounts, the gateway can obscure who initiated the request, what context justified it, and whether the action should have been allowed at all.

That creates a governance gap: security teams may see a clean gateway log while missing the real identity decision behind the request. This is exactly the kind of blind spot described in Ultimate Guide to NHIs, where non-human access often outgrows the governance model used for human users. The issue is not the gateway itself, but the tendency to treat it as a network control rather than an identity enforcement point. Current guidance from NIST Cybersecurity Framework 2.0 and the OWASP Agentic AI Top 10 supports stronger authorization and traceability around AI actions, especially when those actions can affect data access or system state.

In the field, many teams discover the identity problem only after a gateway has already become the easiest path to overprivileged model access.

How It Works in Practice

An LLM gateway becomes an identity governance problem when it sits between users, applications, and one or more models without preserving trustworthy identity context end to end. In practice, the gateway may authenticate the caller once, then forward requests under a shared integration credential. That pattern is simple to operate, but it collapses distinct identities into one opaque execution path.

For IAM teams, the better model is to treat gateway access as a governed entitlement with runtime policy evaluation. The gateway should receive cryptographic proof of workload identity, not just a static secret. For agents and LLM-driven workloads, current practice increasingly favors workload identity, short-lived tokens, and just-in-time credentials so access exists only for the task at hand. That aligns with the direction described in OWASP Agentic Applications Top 10 and the NIST AI Risk Management Framework, which both emphasize runtime risk decisions over static assumptions.

• Bind each request to a human, service, or agent identity before the gateway makes an allow decision.
• Use short-lived credentials, not durable shared secrets, for model and tool access.
• Evaluate policy at request time based on tenant, data class, tool, and action scope.
• Log the initiating identity, the model context, and the downstream action separately.
• Revoke access automatically when the session, task, or workflow completes.

NHIMG research shows the maturity gap is still real: in the 2024 Non-Human Identity Security Report, 88.5% of organisations said their non-human IAM practices lag behind or are merely on par with human IAM, which helps explain why gateways are often deployed faster than governance can mature.

These controls tend to break down in multi-tenant environments with shared gateway credentials and mixed human-agent traffic because identity attribution becomes too coarse to support trustworthy authorization.

Common Variations and Edge Cases

Tighter gateway control often increases integration overhead, so organisations have to balance stronger identity guarantees against latency, operational complexity, and developer friction. That tradeoff is especially visible when an LLM gateway brokers many models, many tenants, or many autonomous agents with different risk profiles.

One common edge case is a gateway used only for rate limiting or routing. In that scenario, the identity problem is smaller, but it is still present if the gateway can trigger retrieval, tool execution, or data egress. Another variation is a federated environment where the gateway receives identity from an upstream IdP but still mints a shared downstream credential. That may be acceptable for low-risk read-only use cases, but current guidance suggests it is weak for write actions, privileged tools, or regulated data flows.

Security teams should also watch for the “hidden broker” pattern, where the gateway sits inside an agent orchestration layer and silently expands access across tools. The CSA MAESTRO agentic AI threat modeling framework is useful here because it pushes teams to model the full tool chain rather than just the model endpoint. NHIMG’s Top 10 NHI Issues also highlights how shared secrets and poor lifecycle control often sit underneath these failures. Best practice is evolving, but there is no universal standard for gateway identity enforcement yet.

Related resources from NHI Mgmt Group

• How should teams use IAM metrics to improve identity governance?
• Why do automated content pipelines create identity risk for IAM teams?
• Why do service accounts create more governance risk than many IAM teams expect?
• Why do unmanaged devices create an identity governance problem?