Federal Guide

Federal AI Governance Implementation Guide

Federal agencies, contractors, and partners supporting high-accountability environments need a practical sequence for defining authority, review, evidence, and monitoring. Requirements depend on system scope, data type, contract terms, and agency policy.

Purpose

A practical implementation sequence

This guide outlines a practical sequence for federal agencies, contractors, and organizations supporting high-accountability environments.

Actual requirements depend on system scope, agency policy, data type, contract terms, and deployment environment. One implementation pattern does not guarantee compliance with NIST, CMMC, FedRAMP, FISMA, DoD policy, OMB policy, or agency-specific rules.

Sequence

Implementation stages

Define

Mission and use-case definition, risk classification, data classification, and authority-to-operate boundaries.

Diligence

Procurement and vendor diligence, model inventory, and use-case inventory.

Authority

Decision ownership, human review, testing, evaluation, and source traceability.

Evidence

Model provenance, audit evidence, change control, incident response, and records retention.

Operate

Access control, contractor responsibilities, subprocessor visibility, deployment boundaries, monitoring, and periodic reassessment.

Caution

Do not over-claim compliance

Platforms and procedures can support governance programs. Formal compliance determinations belong to the acquiring organization, assessors, and applicable authorities based on the actual system boundary.

Next step

Apply these ideas in an operational workflow

Educational resources explain governance concepts. SmartSolo helps teams operationalize review, authorization, and decision records.

See governed AI execution in a live workflow

Review how SmartSolo coordinates multiple AI models, routes human authorization, and preserves the decision record.