What is an AI Gateway: the simple, controlled approach for enterprises
An AI gateway is a control layer that sits between your tools and AI systems. It lets you enforce policy, route high-risk actions to approval, and capture evidence metadata that explains what happened. The point is not to slow teams down. The point is to make AI use accountable and reviewable.
Why enterprises need a gateway
Enterprises run on defined workflows, ownership, and audit expectations. AI systems introduce variability: different models, different prompts, and different results. A gateway normalizes this variability by putting a consistent set of checks in front of execution. It becomes the place where policy and review live.
Without a gateway, teams tend to add ad-hoc guardrails inside each tool. That creates drift and makes it hard to demonstrate control. With a gateway, policy can be defined once and enforced consistently across workflows.
What a gateway actually does
- Policy checks: Decide if a request is allowed, blocked, or requires approval.
- Approval workflows: Route high-risk actions to a human reviewer.
- Evidence metadata: Log request id, decision, policy references, and timestamps.
- Operational signals: Provide a clear, reviewable record of what happened.
These are governance primitives. They help security, compliance, and IT teams evaluate risk without requiring deep changes to every tool or workflow.
What a gateway does not promise
A gateway is not a compliance certificate and it is not a replacement for organizational policy. It provides controls that are designed to support governance, but the final posture depends on how policies are defined and how the system is operated.
A practical pilot approach
The simplest way to evaluate a gateway is to run a small pilot: one use case, clear success criteria, and a defined approval path. The pilot should answer: can we enforce policy before execution, can approvals work end-to-end, and can we produce evidence that an audit team can review?
This is the model we recommend because it is measurable and low risk. You can read more in the docs: AI gateway overview.
Key questions to ask in procurement
- Where do policy checks happen in the request path?
- How is approval handled for high-risk actions?
- What evidence metadata is recorded, and where does it live?
- What is the default data handling posture?
- How does the pilot define success criteria?
A gateway should make these questions easier to answer, not harder. If the answers are unclear, the scope should stay small until the evidence is solid.
Learn more
Next step
If you want to evaluate a gateway for a pilot, share the use case and risk criteria. We will respond with a scoped plan and decision-ready outputs.