AI Procurement Regulations Every Government Contractor Should Know

Artificial intelligence is quickly becoming part of federal acquisition, contract performance, program management, cybersecurity, intelligence support, and business operations. For government contractors, that creates opportunity. It also creates a new compliance challenge.

The federal government is not approaching AI the same way the commercial market is. Agencies want efficiency, automation, better analytics, and faster decision support, but they also have to manage security, privacy, civil liberties, taxpayer value, data rights, mission risk, and public trust. Contractors that sell AI to the government or use AI while performing government contracts need to understand this policy environment before it shows up in an RFP, a customer question, or a post-award compliance issue.

The most important point is this: there is not one single "AI regulation" that covers every GovCon situation. Instead, contractors need to understand a layered environment that includes OMB guidance, FAR and DFARS clauses, CMMC, FedRAMP, NIST risk frameworks, agency-specific acquisition practices, and special DoD and Intelligence Community rules.

This article breaks down the AI procurement rules and guidance every government contractor should know.

Why AI Procurement Is Different in GovCon

In commercial business, AI adoption often starts with speed. A company identifies a workflow, tests a tool, and scales it if productivity improves. In government contracting, that approach is not enough.

Federal AI use is shaped by policy, acquisition discipline, security boundaries, data sensitivity, and mission assurance. OMB Memorandum M-25-22, issued in April 2025, replaced prior AI acquisition guidance and focuses on three themes: supporting a competitive American AI marketplace, safeguarding taxpayer dollars through performance tracking and risk management, and promoting cross-functional engagement in AI acquisition.

For contractors, that means AI will increasingly be evaluated not only on capability, but also on transparency, data handling, documentation, testing, monitoring, interoperability, and vendor lock-in risk. A proposal that says "we use AI to increase efficiency" is not enough. Government buyers will want to know what the AI does, what data it touches, how it performs, how risks are managed, and what happens when it fails.

AI Procurement Rules Are Context-Specific

Before a contractor can assess AI compliance, it has to answer a few practical questions.

Are you selling an AI system or AI-enabled service to an agency? Are you using AI internally to support contract performance? Will the AI process Federal Contract Information, CUI, Covered Defense Information, PII, classified information, or proprietary customer data? Is the tool cloud-based? Is the customer DoD, IC, civilian, law enforcement, healthcare, or another regulated mission area?

Those answers matter because different rules apply in different situations. M-25-22 applies to AI systems or services acquired by or on behalf of covered agencies, but it excludes AI used incidentally by a contractor during performance when the AI is used at the contractor's option and is not directed or required to fulfill contract requirements. It also does not apply to AI acquired for use as a component of a National Security System.

That does not mean contractors can ignore AI use. The same OMB memo tells agencies to determine when solicitations should require disclosure of vendor AI use in contract performance, especially where the government may not otherwise anticipate that AI is being used.

1. OMB M-25-22: The Core Federal AI Acquisition Guidance

OMB M-25-22 is one of the most important documents for contractors selling AI systems or AI-enabled services to federal agencies. It tells agencies how to structure AI acquisition activities across the lifecycle, from requirements definition through post-award monitoring.

The memo directs agencies to use cross-functional teams during AI acquisition, including acquisition, IT, cybersecurity, privacy, legal, civil rights, civil liberties, data, budget, and program expertise as needed. It also instructs agencies to identify potential risks early and determine whether an AI system is likely to support a high-impact use case.

For contractors, this means AI proposals should be written for more than the technical evaluator. They should be understandable to contracting officers, program managers, cybersecurity staff, privacy officials, legal reviewers, data stakeholders, and mission owners.

M-25-22 also encourages broad market research, product demonstrations, testing in realistic environments, performance-based acquisition techniques, Quality Assurance Surveillance Plans, and contract incentives tied to measurable business or mission outcomes.

Contractors should prepare evidence in advance. That includes a plain-English product description, use case boundaries, data flow diagrams, model or system documentation, testing results, risk management approach, human oversight plan, cybersecurity controls, and performance metrics.

The memo also tells agencies to address vendor lock-in, intellectual property rights, use of government data, M-25-21 compliance requirements, and ongoing testing and monitoring in contract terms. A contractor that can explain portability, licensing, data rights, model updates, documentation, and monitoring will be better positioned than a contractor that only describes features.

2. OMB M-25-21: High-Impact AI and Risk Management

OMB M-25-21 governs federal agency AI use and is especially important because M-25-22 links acquisition expectations back to M-25-21 compliance.

The memo defines "high-impact AI" as AI whose output serves as a principal basis for decisions or actions with legal, material, binding, or significant effects on areas such as civil rights, civil liberties, privacy, access to critical government resources, human health and safety, critical infrastructure, public safety, or sensitive and classified federal information.

This matters for contractors because high-impact AI can trigger additional documentation, testing, monitoring, and oversight expectations. M-25-21 requires agencies to implement minimum risk management practices for high-impact AI, including pre-deployment testing, AI impact assessments, ongoing monitoring, human training, and appropriate human oversight.

For GovCon firms, the competitive advantage is not just having AI capability. It is having AI capability that can survive government risk review.

3. OMB M-26-04: LLM Procurement and Unbiased AI Requirements

Large language models now have procurement-specific expectations. OMB M-26-04, issued in December 2025, applies to procured LLMs and requires agencies to include contractual requirements addressing compliance with Unbiased AI Principles in solicitations or orders for LLMs issued after the memo. Agencies were also required to update procurement policies by March 11, 2026, to include those requirements.

For contractors, the most practical impact is documentation. When procuring an LLM, agencies must obtain enough information from the vendor to determine whether the LLM complies with the required principles. Agencies may request acceptable use policies, model, system, or data cards, end-user resources, and mechanisms for end-user feedback.

Depending on the use case, agencies may also request information on pre-training and post-training activities, model evaluations, enterprise controls, output provenance, third-party modifications, and red teaming or other evaluation methods. Relevant requirements may be identified as material to eligibility and payment, which raises the stakes for corrective action and compliance.

Government contractors that provide LLM-based tools should begin building a repeatable LLM documentation package. At minimum, that package should include the model source or provider, intended use cases, prohibited use cases, user guidance, evaluation approach, known limitations, feedback process, update process, security controls, and a point of contact for compliance questions.

4. FAR 52.204-21: Basic Safeguarding of Federal Contract Information

AI compliance does not replace existing cybersecurity and information protection obligations. If an AI tool processes, stores, transmits, or summarizes Federal Contract Information, contractors need to consider FAR 52.204-21.

FAR 52.204-21 defines Federal Contract Information as non-public information provided by or generated for the government under a contract, excluding public information and simple transactional information. It requires contractors to apply basic safeguarding controls to covered contractor information systems and to flow the clause to certain subcontractors.

This is directly relevant to AI because employees may be tempted to paste contract documents, customer emails, meeting notes, deliverables, or program data into AI tools. If that information is Federal Contract Information, the contractor must ensure the system and workflow meet applicable safeguarding requirements.

A practical rule for contractors: do not allow AI use with government contract information unless the tool, account, data handling process, and contract terms have been reviewed and approved.

5. DFARS 252.204-7012: Covered Defense Information, NIST SP 800-171, and Cyber Reporting

For DoD contractors, DFARS 252.204-7012 is one of the most important clauses to consider before using AI with contract data.

The clause applies to Covered Defense Information and covered contractor information systems. Covered Defense Information includes certain CUI and controlled technical information that is provided to the contractor by DoD or collected, developed, received, transmitted, used, or stored by or on behalf of the contractor in support of contract performance.

The clause requires adequate security and, for covered contractor information systems not operated on behalf of the government, points to NIST SP 800-171. It also requires that external cloud service providers used to store, process, or transmit Covered Defense Information meet security requirements equivalent to the FedRAMP Moderate baseline and comply with cyber incident reporting and related requirements.

DFARS 252.204-7012 also requires contractors to rapidly report certain cyber incidents to DoD and flow the clause to subcontractors when subcontract performance involves Covered Defense Information or operationally critical support.

The AI implication is significant. If a contractor uses an AI platform to process DoD CUI or technical data, that platform may become part of the contractor's covered environment. That affects tool selection, access controls, logging, vendor terms, cloud authorization, incident response, and subcontractor management.

6. CMMC: AI Use Can Affect the Compliance Boundary

CMMC is not an AI-specific program, but it is highly relevant when AI touches FCI, CUI, or DoD contract performance. DoD's CMMC Phase 1 implementation began November 10, 2025, and the first phase runs through November 9, 2026, focusing primarily on Level 1 and Level 2 self-assessments.

For contractors, the question is whether AI tools are inside or outside the CMMC assessment boundary. If employees use an AI application to process CUI, generate deliverables from CUI, summarize controlled documents, or store prompts and outputs containing regulated information, that tool may need to be treated as part of the environment.

Contractors should review AI tools against CMMC scoping, system security plans, access controls, audit logging, data retention, incident response, and subcontractor flow-down requirements. Shadow AI use can create compliance risk because leadership may not know that employees have moved sensitive data into unapproved systems.

7. FedRAMP and Cloud-Based AI

Many AI tools are cloud-based, which makes FedRAMP relevant. FedRAMP announced AI prioritization effective August 18, 2025, for certain AI-based cloud services that provide conversational AI engines for routine and repeated federal worker use. The prioritization criteria include enterprise-grade features such as single sign-on, SCIM provisioning, role-based access control, and real-time analytics.

For contractors, FedRAMP authorization or prioritization should not be treated as a blanket approval for every use case. Contract-specific data restrictions still matter. A tool may be appropriate for public information but not for CUI, classified information, export-controlled data, law enforcement sensitive information, source selection information, or agency-specific restricted data.

The right question is not only "Is this tool FedRAMP authorized?" The better question is "Is this tool approved for this contract, this data type, this user group, and this workflow?"

8. NIST AI Risk Management Framework

The NIST AI Risk Management Framework is voluntary, but it is one of the most useful organizing tools for responsible AI adoption. NIST describes the AI RMF as a voluntary framework designed to help organizations incorporate trustworthiness considerations into the design, development, use, and evaluation of AI systems. Its core functions are Govern, Map, Measure, and Manage.

Contractors can use the AI RMF to structure their own AI governance program. "Govern" assigns accountability. "Map" identifies the use case, context, stakeholders, and risk. "Measure" tests performance and reliability. "Manage" monitors, mitigates, and improves the system over time.

For proposal purposes, aligning internal AI governance with NIST language can help contractors communicate maturity in a way that federal stakeholders recognize.

9. DoD and IC-Specific AI Requirements

DoD and Intelligence Community environments require additional care because AI may intersect with national security systems, classified information, operational risk, intelligence data, or mission decisions.

The DoD Artificial Intelligence Cybersecurity Risk Management Tailoring Guide applies to AI systems operated by DoD or on behalf of DoD by a contractor or other entity. It also establishes cybersecurity risk management guidance for the acquisition, development, use, sustainment, monitoring, and disposal of AI systems.

The guide makes an important distinction: AI models themselves do not need an Authority to Operate, but the actual system infrastructure layer does. AI models need cybersecurity evidence developed through an assessment process, including change management documentation, acquisition documentation, and test and evaluation results.

For the Intelligence Community, ICD 505 establishes policy for governance and management of AI developed, acquired, or used by or on behalf of the IC. The directive includes expectations for accountability, auditability, AI provenance, model registry documentation, and risk management.

Contractors operating in DoD and IC environments should assume that AI will require stronger documentation, stricter data handling, closer cybersecurity review, and more disciplined lifecycle management than commercial AI deployments.

What Contractors Should Build Now

Government contractors should not wait for an RFP to ask about AI. By then, the response window is already too short.

A strong GovCon AI readiness package should include:

• An AI use policy that defines approved and prohibited uses.
• An AI inventory showing tools, owners, vendors, data types, and use cases.
• A data handling matrix for public data, proprietary data, FCI, CUI, CDI, PII, export-controlled data, classified information, and customer-specific restricted data.
• A vendor risk review process for AI tools.
• Model, system, or product documentation for AI-enabled offerings.
• Testing and evaluation evidence.
• Human review and escalation procedures.
• Incident response procedures for AI-related data exposure or system failure.
• Subcontractor AI disclosure and flow-down language.
• CMMC and FedRAMP alignment evidence where applicable.
• A process for monitoring model updates, performance changes, and new risks.

This does not have to be overly bureaucratic. It does need to be real, documented, and usable.

Common AI Procurement Mistakes to Avoid

The first mistake is treating AI as a productivity tool without checking data restrictions. If employees are entering government contract information into public or unapproved tools, the company may already have a compliance problem.

The second mistake is assuming vendor terms solve everything. Contractors need to know whether prompts, files, outputs, metadata, or user activity can be stored, reviewed, reused, or used for model improvement.

The third mistake is ignoring AI used by subcontractors. If a subcontractor uses AI to support contract performance, the prime may still need visibility into that use.

The fourth mistake is failing to document testing. AI performance claims need evidence, especially when the AI supports mission, compliance, cybersecurity, public-facing services, or high-impact decisions.

The fifth mistake is overlooking lifecycle management. AI systems change. Models are updated. Data changes. Users adapt. New risks appear. Contractors need monitoring and reassessment after deployment.

The Bottom Line

AI procurement is becoming a trust test for government contractors.

Agencies want AI that improves performance, reduces burden, and supports mission outcomes. But they also need contractors that understand acquisition rules, data boundaries, cybersecurity, documentation, oversight, and mission risk.

The contractors that win in this environment will not simply be the ones with the most advanced AI tools. They will be the ones that can show the government how AI is governed, secured, tested, monitored, and aligned to contract requirements.

GS Consulting helps government contractors assess AI readiness, prepare AI governance documentation, evaluate compliant use cases, strengthen proposal positioning, and build practical implementation roadmaps for DoD, IC, and federal environments.

Suggested Future Reading

• The Ultimate Guide to AI Integration in Government Contracting
• How DoD Contractors Can Use AI Without Putting CUI at Risk
• AI Disclosure in Federal Contracts: What GovCon Firms Should Prepare For
• AI & Automation in Government Contracting Insights Hub