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.
At a glance
What this is: This guide compares LLM gateway options and concludes that routing is not enough without identity-aware access controls, logging, and policy enforcement.
Why it matters: IAM, NHI, and AI platform teams need to treat LLM access as a governed request path because model calls can carry sensitive data and initiate downstream work.
Context
LLM gateways are control layers that sit between applications and model providers to standardise how requests are routed, logged, and governed. The security gap in this article is that many gateways focus on abstraction and performance while leaving identity-aware enforcement weak or optional.
For IAM and NHI programmes, the key issue is not whether teams can call multiple models through one interface. It is whether authentication, authorization, scoped keys, and request-level logging are applied close enough to the request path to govern who or what is using the model, under what context, and with what limits.
Key questions
Q: How should security teams govern access to LLM gateways?
A: They should treat the gateway as the enforcement point for authentication, authorization, and audit logging. That means identifying the human, application, and non-human identities behind each request, applying policy centrally, and retaining complete request evidence for review. If governance sits only in app code, it will not scale across multiple providers.
Q: Why do LLM gateways create access control risk if they only handle routing?
A: Routing alone does not answer who may use a model, what data they may send, or what downstream action a response may trigger. Without identity-aware policy, the gateway becomes a convenience layer that can still carry sensitive data or delegate privilege through shared access paths.
Q: What are the signs that LLM gateway controls are too weak?
A: Warning signs include shared keys across teams, missing per-request audit trails, inconsistent authorization between models, and no clear owner for policy decisions. If usage can move between providers without a matching identity check, governance is probably happening too late in the request path.
Q: What is the difference between an LLM gateway and identity-aware access control?
A: An LLM gateway normalises how applications reach models, while identity-aware access control decides whether the request should be allowed at all. They solve different problems, and treating them as interchangeable leaves policy enforcement too close to the application layer.
Technical breakdown
Unified LLM gateways reduce integration sprawl
A unified LLM gateway provides a consistent interface across multiple providers so teams do not need bespoke code for every model endpoint. That usually means shared routing, retry handling, fallbacks, and usage logging across OpenAI, Anthropic, Google, and other providers. The architectural value is standardisation, but standardisation alone does not equal governance. If the gateway only normalises API calls, it still leaves identity, authorization, and data handling decisions scattered across application code and downstream services.
Practical implication: treat the gateway as a traffic control point, not a complete security boundary.
Access management must be identity-aware, not just API-aware
The article makes clear that many LLM gateways stop at transport and rate control, while the real risk sits in who can invoke a model, what context they bring, and which actions the response can trigger. Identity-aware policy enforcement means binding requests to a user, service, or agent identity and evaluating scope at request time. That matters when the same application can route to different models with different data exposure, cost, and downstream effects. Without this, the gateway becomes a convenience layer rather than an access control layer.
Practical implication: enforce authorization and scoped credentials at the gateway edge, not only in application code.
Logging and observability are governance controls, not just diagnostics
In this article, logging is not presented as a developer convenience. It is part of control enforcement because LLM traffic can carry sensitive content and later trigger actions in agent-based systems or internal tools. Audit trails need metadata about identity, model target, policy decision, and usage context so teams can reconstruct who accessed what and why. That is especially important when multiple providers and proxies sit in the path, because raw model telemetry alone rarely proves governance intent or accountability.
Practical implication: log identity, policy outcome, and request metadata together so reviews can tie usage to accountable subjects.
Threat narrative
Attacker objective: The objective is to use legitimate LLM access paths to reach sensitive data, higher-privilege capabilities, or downstream actions without proper authorization.
- Entry occurs when an application, user, or agent sends model requests through a gateway without strong identity binding at the edge.
- Credential or policy abuse follows when shared or loosely scoped access lets requests reach sensitive model capabilities or downstream services.
- Impact occurs when the model interaction exposes data, bypasses intended limits, or triggers unintended actions in connected workflows.
Breaches seen in the wild
- DeepSeek database exposure 2025: An unauthenticated DeepSeek ClickHouse database exposed over a million log lines with plaintext chat history and API keys in 2025.
- LiteLLM PyPI package breach: LiteLLM PyPI supply chain attack, credentials stolen from users.
Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.
NHI Mgmt Group analysis
Identity-aware control has become the real differentiator in LLM gateway design: routing abstraction solves portability, but it does not solve who is allowed to invoke which model under which context. Once model calls can carry sensitive content or trigger downstream work, authorization becomes a request-path control rather than an application afterthought. Practitioners should treat gateway placement as a governance decision, not only an architectural one.
Access control for LLM traffic fails when the gateway is treated as a transport layer only: the article correctly points to authentication, authorization, and scoped API keys, but those controls have to be evaluated at the same layer where requests are normalised and forwarded. If policy lives elsewhere, identity context gets diluted across proxies, agents, and internal services. The practitioner lesson is that control locality matters as much as control strength.
LLM gateways are becoming policy enforcement points for AI usage: that makes them part of the identity stack, not just the application stack. This is where teams can align model access, request logging, and usage limits with existing IAM and NHI governance, especially where service accounts or agentic workflows call models on behalf of users. The practical conclusion is to govern the gateway as a first-class identity boundary.
LLM access now carries a credential blast-radius problem: scoped keys, provider tokens, and gateway credentials can all expand exposure when multiple models and proxies are chained together. The article implies a governance model in which one weak control can turn model convenience into cross-provider reach. Practitioners should narrow entitlement scope before they widen model access.
Named concept, identity-aware LLM gateway boundary: the effective security boundary is no longer the model provider alone, but the point where identity, policy, and request routing converge. That boundary determines whether a call is merely proxied or actually governed. Teams that have not defined it are already relying on implicit trust across their AI stack.
From our research library:
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
- 13% of organisations suffered breaches of their AI models, according to IBM's Cost of a Data Breach 2025 report.
- Read next: AI Agent Authorisation Guide
What this signals
Identity-aware LLM gateway boundary: once model access can be shared across multiple providers, the security boundary shifts from the model endpoint to the point where identity and policy are enforced. Teams that keep thinking in terms of routing alone will miss the control point that actually governs misuse.
Gateway design is now part of IAM architecture for AI workloads. If a request can reach a model, be logged, and then trigger downstream action, the question is not whether traffic moved successfully. The question is whether the subject, scope, and policy decision were all preserved end to end.
For practitioners
- Define the gateway as an access control point Place authentication and authorization decisions at the same layer that routes model requests so identity context is not lost downstream.
- Scope credentials by user, service, or agent Issue credentials that limit which models, environments, and request types a subject can reach, then review those scopes as model use expands.
- Log identity and policy outcomes together Capture who made the request, which model was targeted, what policy decision was applied, and what metadata accompanied the call.
- Separate routing from governance decisions Use the gateway for abstraction and reliability, but keep policy ownership with IAM, NHI, or platform security teams that can define accountability.
Key takeaways
- LLM gateways reduce integration sprawl, but they do not by themselves establish who is authorised to use a model or what that call may trigger.
- The governance gap is identity-aware enforcement at the request path, not simply the absence of another routing layer.
- Teams need request-level logs, scoped credentials, and policy decisions tied to identity if they want model usage to remain accountable.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and MITRE ATT&CK address the attack and risk surface, while NIST CSF 2.0 sets the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-04 — Insecure Authentication | The article centres on authenticating users, services, and agents at the LLM gateway edge. |
| NHI-05 — Overprivileged NHI | Scoped keys and gateway permissions are core to limiting model access and downstream reach. | |
| Recommendation — Bind LLM requests to strong identities and reject calls that cannot be authenticated at the gateway. Reduce gateway and API key scope so each subject can reach only the models and actions it needs. | ||
| NIST CSF 2.0 | PR.AA-05 — Access Permissions, Entitlements and Authorizations | The article is fundamentally about enforcing authorization close to the LLM request path. |
| DE.CM-09 — Monitoring for anomalous activity | Request logging and observability are presented as necessary governance controls for LLM traffic. | |
| Recommendation — Apply PR.AA-05 to validate permissions before model requests leave the gateway. Monitor gateway logs for unusual model access patterns, policy bypass attempts, and scope drift. | ||
| MITRE ATT&CK | TA0006;TA0008 — Credential Access; Lateral Movement | Weak gateway controls can let attackers reuse model access paths to reach broader systems and data. |
| Recommendation — Map shared LLM credentials and downstream reach to credential abuse and lateral movement detections. | ||
Key terms
- Identity-aware gateway: An identity-aware gateway is a control layer that verifies identity, applies authorization policy, and records audit data before forwarding a request. For AI systems, it becomes the enforcement point between the orchestrator and internal services, preventing the model from becoming the authority for access decisions.
- Scoped API key: A scoped API key is a machine credential whose rights are limited to a defined set of actions or resources. In NHI governance, scope is the control that keeps a valid key from becoming broad standing access, especially when the key is used by scripts, services, or AI agents.
- Request-Path Enforcement: Request-path enforcement means the system that understands a security event also makes the blocking or step-up decision at runtime. In practice, this keeps detection context and action together, which is critical when abuse depends on correlation across sessions, identities, or behaviours.
- Model Access Policy: A model access policy defines which users or groups can call which AI models. It is a governance control, not a routing setting, because it determines the boundaries of permitted use. Strong policies help prevent overbroad access, reduce misuse, and support auditability across model consumption.
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 building or maturing an IAM programme, it is worth exploring.
Published by the NHIMG editorial team on June 11, 2026.
Updated on October 10, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org