When to Stop Extending Your LLM Gateway and Buy a Control Plane: A Decision Checklist

What a gateway is designed to do

An LLM gateway is a legitimate and often necessary component. Products described as AI gateways typically route LLM traffic across providers, apply input and output security controls, detect prompt injection and sensitive data exposure, and provide centralized logging, policy management, and usage visibility. Those are traffic-level functions, and a gateway placed on the network path to model providers is well suited to them.

The pressure starts when the question changes from "what was sent to the model" to "what was the AI allowed to do." Agents call tools, coding assistants touch repositories, and third-party MCP servers expose actions the model never sees as plain text. A component that observes model traffic cannot automatically stop an action it never sees. Whether your gateway can depends on where it sits and what it intercepts, which is the first thing to establish before writing more custom code.

Separating what the gateway handles from what belongs in the execution path

A useful way to draw the line is by action. If the control applies to a request on its way to a model or a response on its way back, a gateway is a reasonable home for it. Output screening is a good example: scoring a model response against a policy before it is returned to the user or passed to a downstream tool is a primary defense against prompt injection.

If the control applies to what an agent does next, the enforcement point has to be where that action executes. OWASP lists runtime enforcement products separately from gateways, describing them as intercepting tool calls and API actions at execution time and applying intent validation and authorization to prevent unsafe or out-of-scope operations. OWASP also treats excessive agency as a named risk, and it publishes dedicated guidance for using third-party MCP servers. Scope limits and approval for agent actions follow from that framing.

The practical test is to pick one agent action, such as a tool call that writes to a system of record, and trace where your current stack can see it, alter it, and stop it. If the honest answer is "only after the fact," the control is observation, not enforcement.

Why evidence changes the build-versus-buy math

Custom redaction and approval logic can be built and kept correct by a capable team. The harder part is producing evidence that holds up when someone else examines it. A regulated enterprise may need to show what its AI was permitted to do and what it actually did, and those are different facts.

OWASP guidance recommends recording structured policy decisions (allow, block, or human review) separately from whether the legitimate task completed. That separation lets a reviewer reconstruct the policy in force at the time, the decision taken, and the outcome. A gateway log that mixes request metadata with application errors rarely supports that reconstruction without additional work.

Regulation adds weight to this. The EU AI Act, Regulation (EU) 2024/1689, was adopted on 13 June 2024. Confirm with counsel which obligations apply to your systems and when, and treat evidence design as a requirement from the start rather than something added before an audit. Retention periods, storage location, and who can read the records are design decisions, and custom extensions tend to leave them for later.

What in-the-path enforcement looks like in practice

Trussed AI is built for the case this checklist describes. It enforces policy in the execution path across models, agents, tools, MCP servers, AI sites, and coding assistants, with the outcomes allow, redact, block, rate limit, and escalate for human approval. Enforcement applies to individual interactions and tool calls, and it is enforced at runtime, not reviewed after the fact. Your gateway can continue to handle routing and authentication.

The enforcement itself generates continuous, audit-ready evidence, mapped to HIPAA, NAIC, NYDFS, the EU AI Act, and NIST. Trussed deploys self-managed in the customer cloud, with configurable retention and data residency. It also provides code provenance, covering the origin, licence, and security context of code.

Trussed has run continuously in production in a regulated enterprise for more than 18 months, with 99.95% uptime on live production traffic. It operates more than 100 models under one policy plane and generates more than 30 GB of audit evidence per day for customers in insurance, pharma, healthcare, and education. Its certifications and attestations include SOC 2 Type II, HIPAA, GDPR, FERPA, ISO 27001, and NIST AI RMF. Apply the criteria above to any option, including this one.