FedRAMP AI Compliance Checklist for Request-Level Controls

A FedRAMP AI compliance checklist has to follow the prompt across the authorization boundary. Start with a named user or workload, trace the model endpoint it calls, inspect the policy decision, and retrieve the resulting audit record. That single test exposes gaps hidden by inventories and architecture diagrams. The FedRAMP program uses NIST SP 800-53 controls within defined cloud-service boundaries, so an AI workflow inherits familiar requirements for access enforcement, event logging, configuration control, and continuous monitoring. I use the eight checks below because each one produces evidence an assessor can actually sample.

1. Fix the AI authorization boundary

Document every component that receives or processes federal data during an AI call. Include the application, gateway, model endpoint, retrieval service, and storage used for prompts or responses. Record each service's authorization status and impact level. Then run a real request and confirm that its network destination matches the diagram. A box on page 47 of an SSP is only useful when traffic behaves the same way. Any model endpoint outside the approved boundary needs a documented decision and a control that prevents regulated data from reaching it.

2. Maintain a route-level model inventory

An inventory should identify providers, models, regions, API routes, owners, data classes, and approval state. Surveys capture declared tools but miss temporary pilots and code-level route changes. Generate the inventory from observed, authorized AI traffic and reconcile it with configuration management. Tag deprecated endpoints and set a removal date. The inventory becomes evidence for the boundary and a source for policy. If a route is absent from the approved set, the request should fail closed before federal data crosses the boundary.

3. Propagate authenticated identity

Shared model API keys identify an application rather than the human or workload initiating the request. Carry enterprise identity context to the AI enforcement point, including role and relevant authorization attributes. NIST SP 800-53 Rev. 5 places account management and access enforcement in the AC family. Test identity propagation by selecting one user, making a model call, and retrieving a record that names that principal. A generic service account fails the attribution test even when authentication succeeded earlier in the application.

4. Enforce least privilege on each request

Define which roles can use each model, route, action, and data class. Apply the rule before the request reaches the LLM. A research user may be permitted to summarize public material while a claims workflow can send controlled records only to an approved endpoint. Test a permitted and denied case for every sensitive route. My opinion is that a checklist without a denial test is paperwork, because it verifies configuration while leaving enforcement unproven. Keep the denial event and its reason code in the evidence set.

5. Classify prompt and response data

Document the categories the control detects, the handling rule for each class, and the action taken on uncertain classification. Evaluate prompt content at the request boundary and response content where the workflow requires it. Avoid retaining raw sensitive text as a default audit field. Store the classification, policy decision, and a protected reference or hash when that meets the evidence need. This creates proof that the organization evaluated data before transmission without building a second repository full of federal records.

6. Generate AU-aligned event records

Map inference calls and policy decisions to AU-2, AU-3, and AU-12. Each event should include a unique ID, time, source, authenticated identity, destination model, policy version, data classifications, and outcome. Send the record to an independent write path so the application under review cannot quietly alter its own evidence. The companion FedRAMP AI audit evidence guide describes the fields and sampling process in detail.

7. Test integrity, retention, and retrieval

Define retention according to the agency's system requirements and incident procedures. Protect records against unauthorized modification, synchronize time sources, and document export formats. Run a tabletop query using a specific date, identity, and model. The team should retrieve the matching decisions in minutes, including denied requests and the active policy version. A screenshot pasted into a ticket can support context, but structured signed records provide repeatable evidence across quarterly assessments and incident reviews.

8. Monitor model and policy changes

Continuous monitoring should alert on new destinations, unexpected models, policy failures, and privileged changes. Connect policy versions to approvals and deployment records. Compare the observed traffic inventory with the authorized inventory on a fixed cadence. The NIST AI RMF can inform risk ownership, while the FedRAMP control baseline governs the assessed system. A useful dashboard points to the underlying event, rather than replacing it with an aggregate count.

DeepInspect

DeepInspect provides an inline policy decision point for HTTP AI traffic. It binds authenticated identity to each request, classifies data, restricts model destinations, and applies per-role and per-route policy before the call proceeds. A tamper-evident record preserves identity, model, policy version, classification, outcome, and timestamp for assessment sampling.

The checklist still requires SSP updates, provider authorization review, endpoint controls, and agency governance. DeepInspect covers the request-path enforcement and evidence portion when AI traffic passes through its boundary. If your control workbook needs a request-level mapping, let's talk today.