Is AI egress control the same as blocking access to ChatGPT?

No, and blocking is usually the weakest version of it. A hard block on consumer AI domains pushes people to personal devices and unmanaged accounts, which removes the last bit of visibility you had. Egress control keeps the sanctioned paths open and puts identity, classification, and a policy decision on each request, so a marketing user can summarize public copy while a request carrying a customer record from the same person is redacted or denied. The goal is a governed path, not a wall.

Does inspecting the prompt add latency users will notice?

Inline enforcement adds a fixed cost to each request, and in production tests that overhead measures under 50 ms. The model's own inference time runs from roughly 500 ms to 5 seconds, so the policy decision resolves inside a window the user already spends waiting for the model. The perceptible latency of an AI feature is dominated by generation, not by an identity check and a content scan in front of it.

How is this different from the model provider's own logging?

The model provider logs what it received, under the account key it received it on. That record lives in the provider's tenant, is scoped to the shared credential rather than the human or agent behind the call, and is not written into your system of record. Egress control produces the log on your side of the boundary, bound to your identities and your policy versions, which is the artifact an auditor or an incident responder can actually work from.

What about agents that call models without a human in the loop?

Autonomous agents are exactly why identity has to travel with the request. An agent acting on its own still needs an attributable identity, a scope of what it is allowed to send and to which endpoints, and a per-action record. Treated as just another authenticated caller through the same enforcement point, an agent's model traffic is governed by the same per-request policy and produces the same audit trail as a human's.

Where should the enforcement point sit in our architecture?

In the request path between your applications and the model APIs, positioned so that every sanctioned route to a model passes through it and unsanctioned direct egress to model domains is closed at the network layer. A stateless proxy is the common pattern because it adds no session state to maintain and can scale horizontally with request volume. The design goal is that there is no supported way to reach a model endpoint except through the point that applies policy and writes the record.

AI Egress Control: Governing the Outbound Traffic Between Your Apps and LLMs

Every time an employee pastes a customer record into a chat assistant, or an internal agent calls an external model to summarize a contract, data leaves your environment inside an HTTPS request to an API you do not operate. That request is egress. Most security teams watch inbound traffic closely and then let outbound model calls ride through the same firewall allowlist that permits api.openai.com for the entire company, with no record of who sent what or what was in the body.

I want to walk through what AI egress is at the request layer, why the standard outbound controls step over it, and the architecture that attaches an identity and a policy decision to every model call before the bytes leave the building.

What AI egress is

Egress, in network terms, is any traffic that originates inside your perimeter and terminates outside it. AI egress narrows that to one destination class: the model endpoints your applications, agents, and staff reach over HTTP. A request to api.anthropic.com/v1/messages or an Azure OpenAI deployment is AI egress. So is a background job that sends a batch of support tickets to a summarization model at 2am.

The part that matters for security sits in the request body. That body carries the prompt, and the prompt carries whatever the caller decided to include: a paste of a patient's chart, a block of proprietary source, a spreadsheet of salaries, a contract under negotiation. The model returns a response, but the exposure already happened on the way out.

Why network egress controls step over AI traffic

The controls most teams already run were built for a different problem, and they treat AI endpoints the way they treat any other approved SaaS domain.

Domain allowlisting is the first gap. A firewall rule that permits traffic to api.openai.com permits all of it, from every service and every person, regardless of what the prompt contains. The rule answers "may this host be reached," not "may this identity send this content right now."

TLS is the second gap. The connection to a model API is encrypted, so a proxy that inspects only the SNI header sees the destination and nothing else. Without terminating the session and reading the payload, the prompt is opaque to the network layer.

Shared service credentials are the third gap, and the one I care about most. Most enterprise AI calls authenticate with a static API key issued to an application. Fourteen internal services behind one key look identical on the wire. When a prompt leaks the salary table and the incident call runs past 11pm, the one question nobody can answer from the logs is which service, and which person behind it, actually sent that request.

Domain allowlisting for AI endpoints stops being a control the moment two teams share a key. That is an opinion a governance committee would soften, and I will keep it sharp: an allowlist that cannot name the sender is inventory, not enforcement.

What egress control on AI traffic actually requires

Real control over AI egress operates on the individual request, not the destination host. Four things have to happen between the caller and the model.

First, an identity travels with every request. Not the application's shared key, but the authenticated user or agent on whose behalf the call is made, carried as context the enforcement point can read.

Second, the content of the prompt is classified before it leaves. Detection for PII, secrets, and regulated data runs against the actual body, so a request that contains a full record can be redacted or denied rather than logged after the fact.

Third, a policy decision is made per route and per role. The same model endpoint can be open to one team for one data class and closed to another, and the decision resolves to permit, redact, or deny at call time.

Fourth, each decision writes a record. Identity, role, policy version, data classification, outcome, and timestamp, committed independently of the calling application so the application cannot suppress it.

All four have to run inline, which means in the request path before the model responds. The budget is available. End-to-end enforcement overhead measures under 50 ms in production tests, against an LLM inference time that runs from 500 ms to 5 seconds. The decision fits inside the latency the user already waits through.

Egress control, DLP, and the API gateway are not the same box

Teams sometimes assume an existing tool already covers this. Two usually come up.

Traditional data-loss prevention watches email, endpoints, and file movement. It rarely terminates the TLS session to a model API and reads the prompt structure, so a paste into a browser-based assistant often never reaches it. Egress control for AI sits in the request path to the model itself.

A conventional API gateway manages inbound traffic to services you host: rate limits, keys, routing for your own APIs. AI egress runs the other direction, toward models you do not operate, and the policy question is about the caller's identity and the prompt's content rather than throughput.

The distinction is not academic. If you map AI egress onto whichever box you already own, you inherit that box's blind spot, and for both of these the blind spot is the prompt.

DeepInspect

DeepInspect is the enforcement point for AI egress. It sits at the request boundary as a stateless proxy between your applications, agents, and staff and any LLM. Every outbound model call passes through it, is evaluated against per-route and per-role policy using the identity context the caller supplies, and is checked for PII and regulated data that gets redacted or blocked before the request reaches the model.

Every call produces a per-decision audit record holding identity, role, policy version, data classification, outcome, and timestamp. The record is tamper-evident and is committed before the response returns, so the calling service cannot quietly drop it. When the next incident call runs late, the question of which identity sent which prompt has an answer.

If your AI endpoints are governed today by a domain allowlist and a shared key, book an AI readiness assessment and we will map where your outbound model traffic is actually going.