Federal AI Governance Implementation Guide

Federal AI governance is implemented as a sequence: define the use case and data, classify risk, assign authority, apply human review where outcomes are consequential, retain provenance and decision evidence, and reassess when models or missions change.

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.

This guide is for federal agencies, contractors, and organizations supporting high-accountability environments. It is educational. It does not replace an Authority to Operate, a contract clause, or counsel.

Smart Logic AI publishes related commercial context on federal AI governance and defense AI governance. This page owns the implementation order of work. For how written policy relates to operational software, see framework vs. platform; for inventory versus authorization evidence, see model vs. decision governance.

Move through these stages in order when standing up a new use case. Skipping evidence design until after go-live is how programs discover they cannot reconstruct a decision.

The NIST AI Risk Management Framework (NIST AI 100-1) is a voluntary framework that many federal programs use as a vocabulary for govern, map, measure, and manage. Current OMB direction for agency AI use is reflected in OMB Memorandum M-25-21, which rescinds and replaces M-24-10. ISO/IEC 42001 describes an AI management system. None of these, by themselves, authorize a system or certify a vendor product.

Prefer precise verbs: a workflow can support implementation of a control family, map to a framework function, or produce evidence relevant to an assessment. Avoid “compliant with” or “certified to” unless the organization has an actual determination.

High-accountability federal work usually requires a named human path before AI-assisted output becomes an official product: a briefing, a recommendation, a capture decision, or an operational action. Use the human-in-the-loop controls, the HITL governance checklist, and an authority matrix to name reviewers and authorizers. Cycle-time measurement design is covered in governed AI workflow performance research.

Retain model provenance and the authorization together. A later inquiry will ask which model version ran and who accepted the residual risk. See AI audit trail requirements and Decision Ledger field guidance for the operational record pattern. Corporate architecture context lives on the AI governance platform page.

Federal capture is a concrete setting where AI-assisted work meets bid/no-bid accountability. Opportunity qualification, proposal drafting, and compliance cross-checks can use models, but the capture decision remains a human authorization problem with an audit expectation.

CaptureIQ is Smart Logic AI’s applied federal capture software for government contracting workflows. It is an example of governed AI applied to capture operations — not a substitute for agency AI governance policy, and not a general-purpose AI governance platform. SmartSolo remains the governed multi-model execution product; CaptureIQ applies accountable workflow to GovCon capture.

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.

An implementation sequence that never reaches runtime review will not survive contact with a live model. Governed AI execution in SmartSolo can apply multi-model comparison, human authorization, and decision records in that runtime path. Pair it with the security and diligence posture on security and compliance and the public readiness narrative in building a compliance-ready SaaS platform rather than treating any single page as a complete ATO package.