How does egress control differ from a network firewall?

A network firewall allows or refuses connections by IP, port, and sometimes hostname. It does not see the agent identity, the natural-person identity, the data classification, or the policy version. An egress allowlist at the AI gateway carries all four and binds them into the audit record. The firewall continues to operate as a coarse network control; the AI gateway adds the request-layer control the firewall cannot see.

What about agents that call hundreds of different tools?

The allowlist is per agent identity, not per tool. A platform agent that calls many tools has a longer allowlist, but the structure is the same: each entry is a destination, a method, an allowed data classification, and a policy version. The catalog of allowed destinations is managed as policy code, reviewed in pull requests, and deployed by the gateway control plane.

Can the allowlist be dynamic per user?

The allowlist can carry conditions that depend on the natural-person attributes (role, region, tenant). The conditions are evaluated by the policy engine at the gateway, not by the agent. This is how the same agent identity calls a tool on behalf of an employee in the EU under one rule set and on behalf of a different employee in another region under a different rule set, while keeping the audit record bound to the verified natural person.

Does this break legitimate agent autonomy?

The agent retains full autonomy within the allowlist. The allowlist scopes the destinations and data classifications the agent is trusted to act on; it does not script the agent's reasoning loop. The trade-off is the same as the principle of least privilege for human identities: agents act freely within the bound their identity is authorized for and are refused outside it.

How does this interact with the OWASP Top 10 for Agentic Applications?

Several entries in the OWASP Top 10 for Agentic Applications 2026 describe failure modes where the agent reaches an unintended destination. The destination allowlist at the gateway is the enforceable control for those entries; the framework-level checks are supplementary.

AI Agent Egress Control: The Destination Allowlist That Survives Prompt Injection

An AI agent makes three kinds of outbound calls. It calls a model API to reason. It calls a tool API to act. It calls an internal service to read or write business data. The egress control problem is to bound which destinations each agent identity can reach, to evaluate the bound at the HTTP layer outside the agent process, and to record the decision in a per-request audit record. The control fails when the destination list lives in the agent's prompt, when the decision happens inside the agent process, or when the record is written by the same process that made the call.

I want to walk through the three classes of egress, the destination-allowlist patterns that survive prompt injection, and the audit-record fields a regulator expects.

The three classes of agent egress

The three classes have different sensitivities and different failure modes. Treat them separately in the policy.

Model calls

The agent calls an LLM endpoint to reason about its next step. The destination is usually a single provider per route: OpenAI, Anthropic, Bedrock, an internal model server. The egress decision is whether the agent identity is allowed to use that model on that route, with that data classification in the prompt. The failure mode is that an attacker reroutes the call to a different model that bypasses the data classification policy.

Tool calls

The agent calls a tool API to take an action. The destination is an HTTP endpoint exposed by the tool. The egress decision is whether the agent identity is allowed to call this tool on behalf of the natural person whose request is in flight, with these parameters. The failure mode is that an injection payload changes the tool's parameters to drain data or to write to an unintended record.

Internal service calls

The agent reads from or writes to an internal database, CRM, ticketing system, or knowledge base. The destination is an internal endpoint. The egress decision is whether the agent identity is allowed to access this dataset with the requested operation. The failure mode is the AI-as-attacker pattern documented in CVE-2026-39987: the agent is the attacker's hands once a pre-auth RCE harvests its credentials.

The destination-allowlist patterns that hold

The allowlist that holds under prompt injection has three properties.

Bound to the agent's verified identity, not to the conversation

The agent's identity is verified at the workload-identity layer (OAuth, SPIFFE, certificate). The conversation is whatever the prompt currently says. A destination allowlist bound to "the agent currently helping user X" can be flipped by injecting a prompt that claims to be user Y. An allowlist bound to the agent's workload identity holds; the injection cannot change the workload identity.

Evaluated at the HTTP layer outside the process

The agent process can be compromised by prompt injection or by a framework-layer escalation. Any allowlist evaluated inside the compromised process is no longer enforceable. The HTTP layer outside the process evaluates the allowlist against the request that actually leaves the process. The allowlist is enforced regardless of what the agent intended.

Defaults to deny

The allowlist starts as empty and is populated by explicit policy rules. The default-deny posture is what the EU AI Act Article 12 record-keeping obligation implicitly requires: a per-decision audit record only makes sense when the decision is non-trivial, which only happens when the default is to refuse. An allowlist that defaults to allow produces audit records that say "permitted" without a defensible reason.

A worked example: customer support agent

The customer support summarization agent has three legitimate egress destinations and a long list of illegitimate ones.

The agent's reasoning loop may produce any of the refused calls under prompt-injection conditions. The gateway refuses them because the destination is off the allowlist for the verified agent identity. The refusal is recorded with the agent identity, the destination, the parameters, and the policy version. The audit record is the artifact the AI Audit Lead reads against EU AI Act Article 19.

The audit-record fields a regulator expects

The audit record for an egress decision has the same shape as any other policy decision the gateway makes. The fields that survive a regulatory inquiry are the ones below.

The natural-person field is the field most deployments fail. The agent identity is the workload running the call; the natural person is the end user the agent acts on behalf of. EU AI Act Article 19 requires the record to identify the natural person involved; an audit record that names only the agent identity is incomplete.

The signature field is what makes the record tamper-evident. A record the agent process can rewrite is not a system of record. The signature is generated by the gateway and verified by the audit pipeline.

How egress control interacts with the rest of the agent stack

The egress allowlist is a layer in a broader defense. Inside the agent process, prompt-injection defenses reduce the chance the agent is tricked into calling an unintended destination. Inside the framework, callbacks may surface obvious malformed requests. At the gateway, the egress allowlist refuses any call to a destination the policy does not permit for the verified agent identity. The three layers compound; the gateway is the enforceable layer because it sits outside the failure domain of the agent process.

DeepInspect

DeepInspect implements destination allowlists per agent identity at the gateway layer. The allowlist defaults to deny. The decision is bound to the agent's verified workload identity, not to the conversation state. The audit record carries the agent identity, the natural-person identifier from the user's verified session, the destination, the data classification, the policy version, and the decision.

The egress control sits on the same policy plane as the in-bound model calls and the tool calls; the platform team manages one set of rules, not one per agent framework. Take the AI readiness self-assessment to see where your current agent egress policy sits against the gateway requirement.