Should AI teams centralise model routing in a gateway?

Yes, if the gateway is controlled like a privileged runtime service. Centralisation improves auditability and consistency, but it also concentrates trust and access. Teams need strict identity controls, environment separation, and logging so the gateway does not become an unchecked control point for model selection and data flow.

Why This Matters for Security Teams

Centralising model routing can make governance easier, but it also creates a high-value control plane that decides which model sees which prompt, which tools are reachable, and where sensitive data flows. That is why this is not just an architecture choice. It is a security design choice with implications for access control, logging, segregation of duties, and incident response. NIST guidance on governance and protection outcomes in the NIST Cybersecurity Framework 2.0 maps well to this problem because the gateway needs clear ownership and measurable controls.

The main risk is false confidence. Teams often assume a single gateway reduces exposure by simplifying policy, but if that gateway can route requests to multiple models, tenants, or data classes without strong identity checks, it becomes a privileged runtime service with broad blast radius. For AI systems, that means the gateway can become the easiest place to misroute regulated data, bypass model-specific guardrails, or conceal shadow access to higher-risk models. In practice, many security teams encounter gateway misuse only after routing exceptions or data leakage have already occurred, rather than through intentional design reviews.

How It Works in Practice

A well-run model gateway acts like a policy enforcement layer rather than a convenience proxy. It should authenticate the caller, authorize the request, evaluate context such as environment, data sensitivity, and approved use case, then route to an allowed model with the minimum necessary privileges. For AI teams, the gateway should also preserve an audit trail of prompt metadata, model version, transformation rules, and policy decisions so that outputs can be traced later. That traceability is important for model risk management and for investigating prompt injection or unsafe tool use, areas where guidance from OWASP Top 10 for LLM Applications remains highly practical.

Operationally, the gateway should be treated as a privileged service with hard boundaries. Useful controls include:

• Separate production, testing, and sandbox routing paths.
• Service-to-service authentication for all model calls, not just user login.
• Policy checks that block unapproved model selection by default.
• Logging that captures who requested routing, what policy allowed it, and which model responded.
• Secret handling that prevents the gateway from becoming a reusable credential broker.

Where agentic systems are involved, the gateway should also govern tool access and not just model choice. That matters because an AI agent with execution authority can turn a routing decision into a downstream action, which is exactly where a service boundary becomes a trust boundary. Current best practice is to keep model selection, tool invocation, and data egress policy under separate controls, even if they are implemented in the same platform. These controls tend to break down when development teams hard-code fallback routing or allow direct model access from application services because policy enforcement becomes inconsistent.

Common Variations and Edge Cases

Tighter central routing often increases operational overhead, requiring organisations to balance governance benefits against latency, dependency risk, and product-team autonomy. There is no universal standard for how centralised the gateway should be, and current guidance suggests the right answer depends on data sensitivity, model diversity, and regulatory exposure. In low-risk experimentation, a lighter gateway may be sufficient. In regulated environments, the gateway should behave more like a controlled identity layer for models than a simple API switchboard.

The edge cases are usually about scope. If one gateway handles both internal copilots and customer-facing AI features, policy drift can follow quickly unless environments are strictly separated. If the gateway performs prompt rewriting, retrieval, and routing in one place, failure analysis becomes harder because a single control point is shaping multiple stages of the workflow. Teams should also be cautious with multi-tenant setups, where model selection may reveal business context or allow one tenant’s policy to bleed into another’s routing path. For broader AI governance alignment, NIST AI Risk Management Framework is useful for structuring accountability, while OWASP Agentic AI Top 10 is especially relevant when the gateway brokers tool-using agents. The practical rule is simple: centralise routing only when the gateway can be operated as a governed control plane, not as a convenience layer.

Related resources from NHI Mgmt Group

• How should security teams govern model routing in AI agent workflows?
• How should security teams govern AI agents that use Model Context Protocol?
• How should security teams govern AI agents using Model Context Protocol?
• How should security teams govern AI use when the same model creates different risk in different contexts?