AI Gateway overview
An AI gateway is a control layer that sits between your tools and AI systems. It enforces policy checks, routes high-risk actions to approval, and records evidence metadata that explains what happened. The goal is not to slow teams down. The goal is to make AI use accountable and reviewable.
Why gateways exist
B2B teams balance speed with accountability. AI can accelerate work, but it can also introduce variability. Different prompts, different models, and different outcomes can lead to unpredictable behavior. A gateway reduces that variability by inserting a consistent decision point before execution.
This decision point allows security, compliance, and IT stakeholders to define what is allowed, what is blocked, and what requires approval. It provides a single place to apply policy and a shared vocabulary for reviews.
The gateway model also makes pilots easier to evaluate. Instead of debating every tool in isolation, the team can focus on one control surface and measure how it handles risk, approvals, and evidence.
What an AI gateway does (claim-light)
- Policy checks: evaluate requests against defined rules before execution.
- Approval routing: send high-risk actions to a human reviewer.
- Evidence metadata: capture request id, decision, policy reference, and timestamps.
- Operational visibility: provide a reviewable record for audits and incident timelines.
These capabilities are designed to support governance, but the exact posture depends on the pilot scope and how policies are defined. The public site does not claim specific compliance certifications.
What an AI gateway is not
- Not a full policy program by itself. Policies still need ownership and review.
- Not a guarantee of compliance. It provides controls that help enable governance reviews.
- Not a replacement for data governance. Retention and handling are defined per pilot scope.
How it fits into a request path
In a gateway model, requests flow through a policy evaluation step before any action is executed. The gateway can decide to allow, deny, or request approval. If approval is required, the request is held until an operator reviews it. Once a decision is recorded, the request can proceed (or be blocked).
This creates a consistent control plane across tools and workflows. It also simplifies audit conversations because decisions can be tied to a policy version and a timestamp.
In practice, gateways are most valuable when they make the decision path predictable. That means the same policy checks and approval logic apply across tools, regardless of which team initiated the request.
Common policy outcomes
- Allow: the request is low risk and proceeds automatically.
- Approval required: the request pauses until a human decision is recorded.
- Deny: the request is blocked because it violates policy or scope.
These outcomes are intentionally simple. They are easy to audit and easy to explain during reviews.
Why evidence-first logging matters
Full-content logging is often unnecessary and can increase risk. Evidence-first logging focuses on the decision context: request id, decision result, policy reference, and timestamps. This is usually sufficient for compliance and incident review without retaining raw content.
Evidence-first logging is also more scalable. It allows teams to review decisions at scale while keeping the data handling posture tight.
Pilot-ready evaluation
A gateway is easiest to evaluate in a small pilot. Start with one use case, define success criteria, and measure outcomes. A pilot should confirm that policy checks occur before execution, approvals are respected, and evidence metadata is available for review.
Once those controls work end-to-end, you can decide whether to expand scope. This keeps risk low and gives procurement a clear path to a go/no-go decision.
FAQ
Is a gateway the same as a firewall?
No. A firewall is a network control. A gateway is an application-level control that evaluates AI requests and captures evidence metadata before execution.
Does a gateway guarantee compliance?
No. A gateway provides controls designed to support governance, but compliance depends on policies, data handling, and contractual terms defined during a pilot or deployment.
How do approvals work?
Approvals are triggered by policy. When a request is marked as high risk, an operator reviews a summary and decides whether to allow or deny.
What data is logged?
Evidence metadata such as request id, decision result, policy reference, and timestamps. Full data handling posture is defined in pilot scope.
Where do we start?
Start with one use case and define clear success criteria. The pilot should validate that the gateway enforces policy before execution and captures evidence metadata for review.