Step 1: Map the AI traffic you need to route
An AI gateway only governs the traffic that flows through it, so the first step is finding all the traffic. Inventory every place an LLM is called: sanctioned tools, embedded AI inside existing SaaS, coding assistants, internal applications that hit a provider API, and the personal accounts staff use when the official tool is slow. For each, record the model provider, the data class involved, and the team that owns it. This map tells you which routes the gateway must cover on day one and which can follow. Deploying a gateway that only the engineering team's API calls pass through, while the rest of the org goes direct, gives you the illusion of control without the substance.
Step 2: Place the gateway as the single egress path
The gateway sits between your organization and every model provider, so that no AI request reaches an external model except through it. Architecturally this means pointing application API calls at the gateway endpoint instead of the provider directly, and, for human tool use, routing through a governed client or proxy. The goal is one egress door. If teams can still call providers directly, your gateway is a suggestion, not a control. Decide early whether you enforce this at the network layer, the identity layer, or both, because a control point that can be bypassed governs only the well-behaved.
Step 3: Add identity and attribution
Every request through the gateway should carry an identity: a named user or a specific service account. Without attribution you can log that a prompt was sent but not who sent it, which fails the first question any auditor asks. Wire the gateway into your existing identity provider so that authentication is inherited rather than reinvented. Attribution is also what makes least privilege possible later, because you cannot scope what a service may do with a model until you know which service is calling.
Step 4: Turn on logging and observation first
Before you block anything, see everything. Configure the gateway to log every request and response, attributed and timestamped, with the model and data class recorded. Run in this observe-only mode for long enough to understand your real traffic: which teams use which models, where sensitive data is showing up, and what your baseline looks like. This stage prevents two mistakes. It stops you from writing enforcement rules against imagined traffic, and it gives you the evidence to show stakeholders what ungoverned usage actually looks like before you change it.
Step 5: Add redaction for sensitive data
Now make the gateway act on what it sees. Configure redaction so that PII, secrets, and other sensitive classes are stripped from the prompt before it reaches the model. This is the highest-value control to enable early, because it reduces the blast radius of every request without blocking legitimate work: the model still answers, but it never receives the identifier or secret it did not need. Test redaction against real prompts from your observation period so you tune it to the data your org actually sends, not a generic pattern set.
Step 6: Enforce policy and fail closed
With observation and redaction in place, add the rules that block. Define which uses are not permitted, which data classes may never leave, and which providers are approved, then have the gateway enforce them at request time. The important design choice is the default: when a request is ambiguous or a rule cannot be evaluated, fail closed and deny rather than letting it through. A gateway that fails open quietly stops governing exactly when it matters. Because the rules live at one control point, a policy written once applies to every provider, including an open-source model a team adds next quarter.
Step 7: Extend coverage and prepare for agents
Deployment is not finished when the first route is governed. Work through the inventory from Step 1 and bring each remaining path through the gateway, closing the direct-access routes as you go. Then look ahead to agents. An autonomous system that takes actions, not just generates text, needs the gateway to intercept its tool calls and enforce least privilege, not only inspect its prompts. A gateway built to observe, attribute, redact, and enforce for LLM traffic is the foundation that agent governance extends, so design step 7 as the bridge from governing prompts to governing actions.
Frequently asked questions
What is an AI gateway?
An AI gateway is a single control point that sits between your organization and every model provider. All AI requests route through it, which lets you attribute each call to a user or service, log it for audit, redact sensitive data before it reaches the model, and enforce policy at runtime across every provider at once.
Should I enforce blocking rules right away?
No. Run the gateway in observe-only mode first so you can log and understand your real AI traffic before writing rules. Enforcing against imagined traffic produces brittle rules and false blocks. Once you can see the baseline, add redaction, then add blocking policy that fails closed on ambiguous requests.
Why does the gateway need to be the only egress path?
Because a control point that teams can bypass governs only the well-behaved. If applications and staff can still call model providers directly, the gateway becomes a suggestion. Enforcing a single egress door, at the network or identity layer, is what makes the gateway an actual control rather than an optional one.
How does an AI gateway relate to agent governance?
A gateway built to observe, attribute, redact, and enforce for LLM prompts is the foundation that agent governance extends. Agents take actions rather than only producing text, so the same control point has to intercept their tool calls and enforce least privilege in real time, not just inspect prompt text.