LLM gateways and access control gaps: what IAM teams need to know

TL;DR: Unified LLM gateways reduce routing complexity across multiple model providers, but the real control gap is identity-aware access management, logging, and policy enforcement, according to Pomerium's analysis. Gateway strategy now matters because model traffic can carry sensitive data and trigger downstream actions, so identity and authorization must move closer to the request path.

NHIMG editorial — based on content published by Pomerium: Best LLM Gateways in 2025: Top Tools for Managing and Securing AI Models

Questions worth separating out

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.

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.

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.

Practitioner guidance

• 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.
• 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.
• Log identity and downstream action together Capture the caller, the policy result, the model chosen, and whether the request triggered a follow-on action.

What's in the full article

Pomerium's full blog post covers the operational detail this post intentionally leaves for the source:

• Vendor-by-vendor comparison of LiteLLM, OpenRouter, Kong AI Gateway, Portkey, and other gateway options
• Feature-level discussion of logging, retries, quotas, deployment model, and model compatibility
• Practical architecture examples for placing an identity-aware proxy in front of an existing gateway stack
• Additional context on why access management becomes the deciding control in AI infrastructure

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

LLM gateways and access control gaps: what IAM teams need to know?

Explore further

View Full Forum →  |  NHI Foundation Course →