AI Gateway for Government Agencies: Identity and Audit for Public-Sector AI

A public-sector team stands up an AI assistant for caseworkers, and it works: staff draft faster, summaries arrive sooner, backlogs ease. What the agency cannot produce is the record its own accountability rules assume exists. Which employee sent which record to the model, whether the data was authorized for that use, and what the system decided are questions federal and state oversight will eventually ask, and the deployment logged a call to a model host and nothing more. Agencies operate under a zero-trust mandate and under records obligations that predate AI and apply to it anyway.

I want to walk through how an AI gateway extends the identity-aware access control and record-keeping an agency already owes to its other systems onto model traffic, so public-sector AI use can be governed and evidenced rather than run on trust.

Public-sector AI inherits obligations the tool ignores

Government AI use does not get a clean slate. Zero-trust direction across federal agencies requires access to be verified per request rather than assumed inside a network boundary. Records laws require that an agency preserve documentation of its decisions and produce it for oversight and public-records requests. The NIST AI Risk Management Framework asks agencies to govern, map, measure, and manage AI risk with evidence. Each of these assumes the agency can see and record what its systems do. An AI tool that authenticates once, runs on a shared key, and logs only that a call happened satisfies none of them.

The result is a governance gap that widens as use grows. A caseworker's model calls are indistinguishable from a colleague's under the shared credential, sensitive records leave with no classification recorded, and the automated summary that shaped a decision has no preserved trail. My view is that public-sector AI should meet the same access and records discipline as any other system handling citizen data, and right now the AI channel is the one exception nobody chose on purpose. The govern, map, measure, manage functions of the NIST AI RMF name exactly the evidence that channel fails to produce.

Identity-aware control at the model call

An AI gateway applies the agency's access model to model traffic. It verifies the individual employee or system behind each call rather than accepting a shared key, classifies the data in the prompt, and applies policy that decides whether that identity may send that class of data to that model. A record protected by statute is blocked from a model the agency has not authorized for it. A call from an employee outside the authorized role is denied. The verification happens per request, which is what a zero-trust posture requires and what applying zero trust to AI traffic means in practice.

That gives an agency three concrete controls on AI use. Identity binds each model call to a named person or system. Classification recognizes protected and sensitive records in the prompt. Enforcement blocks or redacts calls that fall outside authorized use, inline, before the data reaches the model. The agency defines the rules against its own authorities, and the gateway applies them to every call.

The record oversight and records laws assume

Accountability in government rests on the record. A per-decision log naming, for each model call, the identity, the data classification, the model and destination, the policy applied, and the outcome is the artifact that answers an inspector general, a records request, or an internal review. It lets an agency show that AI use stayed inside authorized bounds across a period, and it preserves the trail of automated processing that decisions may later be questioned on. Produced at the request boundary rather than by the application, the record documents AI access independently of the tool, which is the property that makes it hold up under review. It applies the same discipline as what to log for AI compliance to the public-sector setting.

DeepInspect

This is the gap DeepInspect closes for an agency. DeepInspect runs as a stateless proxy between your employees, systems, agents, and any LLM. It binds a named identity to each model call, classifies protected and sensitive data in the prompt, applies per-role and per-route policy with detection and redaction, and writes a tamper-evident per-decision record of identity, data class, policy version, destination, and outcome.

Those records extend an agency's zero-trust access control and its records obligations to AI traffic, and they are produced independently of the applications employees use. DeepInspect does not set your authorities or classify your records for you; it enforces the access rules you define and preserves the evidence your oversight requires. To bring public-sector AI use under identity control and audit, book a vertical briefing for government.