LLM gateways need identity-aware controls, not just routing logic

At a glance

What this is: This is an analysis of unified LLM gateways and the article's key finding is that routing alone is not enough without identity-aware access control and logging.

Why it matters: It matters because IAM, NHI, and agentic AI teams all need to govern who or what can call models, under what policy, and with what audit trail.

👉 Read Pomerium's analysis of the best LLM gateways in 2025

Context

LLM gateways sit between applications and multiple model providers, giving teams one control point for routing, logging, quotas, and policy enforcement. The governance gap is that many gateways optimise for performance and abstraction while leaving access control too thin for production identity risk.

For IAM and NHI programmes, the issue is no longer whether a model can be reached, but whether the calling identity is authenticated, authorised, and observable at request time. That becomes more acute when agents and internal tools can trigger model calls automatically, because the access path can carry data, approvals, and downstream action in the same flow.

Key questions

Q: How should security teams govern access to LLMs used by applications and agents?

A: Security teams should govern LLM access the same way they govern other non-human identities: authenticate the caller, scope the credential, enforce policy before the request is forwarded, and log the decision. If an application or agent can reach a model without those controls, the gateway is only routing traffic, not governing access.

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

A: LLM gateways create an identity governance problem because they sit in the path of sensitive prompts, service-to-service calls, and agent-driven actions. If access is not tied to a clear identity and policy decision, the gateway hides privilege rather than controlling it. IAM teams should treat model access as a governed entitlement, not a simple API endpoint.

Q: What breaks when LLM gateway logging does not capture identity context?

A: When gateway logging omits identity context, teams cannot reliably tell who called the model, which policy allowed it, or whether the request triggered a downstream action. That breaks incident response, access review, and abuse detection. A latency dashboard may show traffic health, but it does not prove that model access was appropriate.

Q: How do organisations avoid over-trusting unified LLM gateways?

A: Organisations should avoid treating a unified gateway as a full security boundary. It may simplify routing and observability, but it still needs identity-aware policy, scoped credentials, and revocation tied to lifecycle events. The control question is whether the gateway narrows access or merely centralises it.

Technical breakdown

Unified LLM gateway architecture

A unified LLM gateway normalises calls to different model providers behind one API surface. It can route requests, retry failures, balance load, and centralise usage logs so teams do not hard-code provider-specific logic into every application. That reduces integration overhead, but it does not automatically solve governance. The security value depends on whether the gateway can enforce authentication, policy evaluation, and request-level metadata before the model call leaves the control plane.

Practical implication: treat the gateway as a policy enforcement point, not just an API shim.

Authentication, authorization, and scoped API keys for model access

Model access control is a non-human identity problem when services, agents, or applications call LLMs on behalf of users. In that pattern, the credential is often an API key or token bound to a workload, not a person, which makes coarse access especially risky. Fine-grained authorization matters because different callers need different models, contexts, and data scopes. Without scoped keys and request-level policy, one compromised integration can reach far more capability than intended.

Practical implication: issue narrowly scoped credentials per workload or agent and separate model permissions by function.

Logging, audit trails, and token exposure in LLM traffic

LLM requests often contain prompts, system instructions, retrieved context, and outputs that may expose sensitive data or operational intent. Central logging helps, but only if it captures enough metadata to answer who called what, with which policy decision, and whether downstream actions were triggered. In identity terms, this is about traceability across non-human identities and autonomous workflows. If logs stop at request volume or latency, they miss the governance evidence security teams need.

Practical implication: log identity, policy outcome, model selection, and downstream action markers together.

NHI Mgmt Group analysis

LLM gateways have become identity control points, not just traffic control points. The article makes clear that the routing problem is secondary to the governance problem: who or what is allowed to call a model, under what context, and with what visibility. Once model access starts carrying sensitive prompts and downstream execution, gateway design becomes an IAM decision as much as an infrastructure decision. Practitioners should treat model access as governed identity traffic, not generic API traffic.

Policy at the gateway matters because model calls are increasingly made by non-human identities. Service accounts, agents, and internal automation often stand behind the application, which means classic user-centric controls are too shallow. The discipline here is consistent with OWASP NHI and Zero Trust thinking, where every request needs a decision, not a blanket trust assumption. Practitioners should separate human sign-in from machine request authorisation.

Unified gateways expose a recurring runtime governance gap: access control is often bolted on after routing. That creates a fragile model where the system knows how to reach a model before it knows whether the caller should be allowed to use it. Runtime authorisation before model invocation: this is the control concept the article surfaces, and it is the line between observability and governance. Practitioners should insist that policy evaluation happens before the request is passed downstream.

The LLM gateway pattern is converging with broader identity-aware proxy design. Pomerium's framing points to a market where security teams increasingly want policy enforcement in front of AI services, not just around them. That aligns with the direction of modern identity architecture: central control points, consistent request decisions, and auditable access paths across human and machine actors. Practitioners should re-evaluate whether their AI access layer is really an identity layer in disguise.

The article reinforces that AI governance fails when access is treated as a transport concern. A model gateway can simplify provider sprawl, but that simplification is only safe if the identity layer is equally explicit. Otherwise teams inherit a clean API surface with weak policy, weak auditability, and weak containment. Practitioners should use gateway adoption as the trigger to reset model access governance.

From our research:

• 96% of organisations store secrets outside of secrets managers in vulnerable locations including code, config files, and CI/CD tools, according to the Ultimate Guide to NHIs.
• Only 5.7% of organisations have full visibility into their service accounts, which means the inventory problem is already constraining governance before AI workloads scale further.
• For a broader control baseline, Top 10 NHI Issues shows why visibility, rotation, and offboarding need to be treated as one operational discipline.

What this signals

Runtime gateway policy will become a standard requirement as LLM usage spreads beyond pilot projects. Teams that let model access sit outside identity governance will find themselves retrofitting controls after usage patterns are already embedded. The practical shift is to fold model endpoints into the same policy and audit model used for other non-human identities, with particular attention to scoped credentials and lifecycle offboarding.

Identity-aware proxy patterns are now relevant to AI access as well as traditional application access. As model traffic becomes business-critical, the control plane needs to answer the same questions every IAM programme should already ask: who is calling, what is allowed, and what evidence exists after the fact. That is why gateway selection should be evaluated alongside NIST Cybersecurity Framework 2.0 and request-level Zero Trust design.

The next governance failure will not be lack of model choice, but lack of entitlement discipline around model choice. If teams cannot separate human approval, workload identity, and automated agent access, they will lose the ability to prove that LLM usage stayed inside policy.

For practitioners

• Enforce request-time authorization at the gateway Require a policy decision before any model request is forwarded, and bind that decision to the calling identity, model, and context. Do not allow the gateway to become a pass-through layer for privileged integrations.
• Issue scoped credentials per workload or agent Use separate API keys or tokens for each service, tool, or agent path so one integration cannot reach all models by default. Tie credential scope to the specific business function that needs it.
• Log identity and downstream action together Capture the caller, the policy result, the model chosen, and whether the request triggered a follow-on action. That gives security teams evidence for investigations and access reviews.
• Review model access as part of NHI governance Fold LLM gateway permissions into your non-human identity inventory, recertification, and offboarding process so stale integrations are removed when tools or workflows change.

Key takeaways

• LLM gateways solve routing complexity, but identity-aware access control is what makes them safe for production use.
• The main governance risk is not model sprawl alone, but the absence of scoped credentials, request-time policy, and audit evidence.
• IAM and NHI teams should treat model access as an entitlement lifecycle problem, not a simple infrastructure integration.

Key terms

• LLM Gateway: A unified control layer that sits between applications and multiple language model providers. It standardises routing, logging, quotas, and request handling so teams can manage model access through one interface rather than building provider-specific integrations everywhere.
• Identity-Aware Proxy: A proxy that makes access decisions based on the identity, context, and policy associated with each request. In AI environments, it helps ensure that model access is authorised and auditable instead of being treated as ordinary network traffic.
• Scoped API Key: A credential that limits what a workload, service, or agent can do when calling an external system. In LLM environments, scoping should narrow model access, data exposure, and allowed actions so one compromised integration cannot inherit broad platform-wide privilege.

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.

This post draws on content published by Pomerium: Best LLM Gateways in 2025: Top Tools for Managing and Securing AI Models. Read the original.