Executive Summary
The federal government's regulatory posture on artificial intelligence has transformed from aspiration to obligation. Executive Order 14110 on Safe, Secure, and Trustworthy AI, OMB Memorandum M-24-10 governing federal AI use, and a constellation of DoD-specific directives have created a binding governance architecture that flows downstream to every organization doing AI-relevant work on behalf of the federal government. For the roughly 200,000 active federal contractors and subcontractors, this is no longer a policy-watching exercise — it is a compliance imperative with procurement, contract performance, and reputational consequences.
This white paper, authored by Kurt A. Richardson, PhD, provides a comprehensive, practitioner-grade guide for federal contractors seeking to understand their AI governance obligations, build compliant governance structures, and position their organizations for the increasingly rigorous AI accountability environment that is already emerging in solicitations, task orders, and contract clauses.
Federal contractors using AI in contract performance — including AI-assisted document generation, predictive analysis, automated testing, and agentic workflow tools — are subject to an expanding web of governance requirements. The contractors who build compliant governance structures now will have a decisive competitive advantage in the AI-enabled contracting environment taking shape over the next 24–36 months. Those who do not will face audit findings, contract performance questions, and potential exclusion from AI-involving task orders.
The AI Governance Imperative
The federal government is now the world's most consequential AI governance actor. Through a combination of executive orders, OMB guidance, agency-specific policy, and emerging contract clauses, the federal government is establishing what will effectively become the global standard for responsible AI deployment in high-stakes environments. Federal contractors sit at the intersection of this regulatory moment — simultaneously subject to these requirements as performers and positioned as the delivery mechanism for the AI capabilities that agencies are deploying.
This is not a future concern. The governance requirements that exist today — OMB M-24-10, the DoD AI Ethics Principles, NIST AI RMF, and agency-specific implementation guidance — are already influencing how proposals are evaluated, how task orders are structured, and how program offices assess contractor AI maturity. The contractors who treat AI governance as a compliance checkbox will lag those who treat it as a strategic capability.
What Changed and Why It Matters
Three structural shifts have moved AI governance from voluntary best practice to contractual obligation for federal contractors:
- Executive Order 14110 (October 2023) directed agencies to implement AI governance structures and to develop mechanisms for assessing AI risk in federal operations — including contractor-performed operations.
- OMB M-24-10 (March 2024) established binding requirements for federal agencies using AI in consequential decisions, including minimum testing, documentation, and human oversight standards. These requirements propagate to AI tools and services procured from contractors.
- DoD AI Adoption Strategy (2023) and the updated DoD AI Ethics Principles implementation guidance establish specific requirements for AI used in DoD programs that go substantially beyond the civilian agency baseline.
The cumulative effect is a governance environment in which any contractor using AI to support contract performance — analyzing data, generating documents, making recommendations, automating workflows — faces a duty of care that did not exist three years ago and that is being operationalized into procurement requirements with increasing speed.
Who This Paper Is For
This paper is written for general counsel, compliance leads, program managers, CIOs, and technical leads at federal contracting organizations of all sizes — from large primes to specialized small businesses like Continuum Resources. It is structured to serve both those beginning to build AI governance capacity and those seeking to assess and mature existing practices against the current and emerging policy environment.
The Regulatory Landscape
The AI governance framework applicable to federal contractors is not a single document — it is a layered, evolving ecosystem of executive orders, OMB guidance, agency-specific policy, and emerging contract clauses. Understanding the structure of this ecosystem is prerequisite to building a compliant governance posture.
Policy Timeline: From Aspiration to Obligation
The Five Governing Frameworks
Federal contractor AI governance is shaped by five overlapping frameworks that must be understood in concert, not in isolation:
| Framework | Issuer | Character | Contractor Relevance |
|---|---|---|---|
| NIST AI RMF 1.0 | NIST | Voluntary / de facto standard | Reference architecture for all contractor governance programs; explicitly referenced in M-24-10 |
| EO 14110 | White House | Directive to agencies | Creates agency requirements that flow to contractors through program requirements and clauses |
| OMB M-24-10 | OMB | Mandatory for agencies | Establishes minimum AI use requirements that agencies must impose on AI tools and services from contractors |
| DoD AI Ethics Principles | DoD / CDAO | Policy for DoD programs | Explicitly applies to AI developed by or for DoD; defense contractors are directly subject to implementation guidance |
| NIST AI RMF Playbook | NIST | Implementation guidance | Provides specific practices for each GOVERN, MAP, MEASURE, MANAGE function — directly applicable to contractor governance build |
What Contractors Are Actually Required to Do
The federal AI governance landscape creates a spectrum of obligations for contractors — from explicit contractual requirements to implicit duties of care that arise from the nature of the work. Understanding the difference between these obligation types is essential for prioritizing governance investments.
Tier A: Explicit Contractual Obligations
These requirements appear or are beginning to appear directly in contracts, task orders, and solicitations. They are non-optional and failure to comply creates contract performance issues.
- AI Use Disclosure: Emerging requirements to disclose when AI is used in contract deliverables, reports, analysis, and recommendations. Some agencies are beginning to require contractor AI use inventories as a contract deliverable.
- Safety and Testing Standards: For AI systems developed under contract, agencies are requiring documentation of testing methodology, bias evaluation, and safety validation aligned to NIST AI RMF standards.
- Human Oversight Documentation: M-24-10's human oversight requirements for consequential AI decisions are flowing into contracts requiring contractors to demonstrate that their AI-assisted deliverables maintain appropriate human review and decision authority.
- Incident Reporting: Several agencies now require contractors to report AI system failures, unexpected behaviors, or adverse outcomes within defined timeframes — analogous to security incident reporting obligations.
- Data Governance: Contracts involving AI systems with access to federal data require documented data handling, retention, and access control practices that address the AI-specific data exposure risks discussed in Continuum's Secure RAG Architectures publication.
Tier B: Implied Duty of Care
These obligations arise from the combination of existing contractor performance standards, the specific nature of AI risk, and the government's reasonable expectation of professional practice. While not yet in explicit contract clauses, failure to maintain these practices creates performance and reputational risk.
- Documenting the AI tools used in contract performance, including version, provider, and purpose
- Maintaining a process for validating AI-generated content before it is included in government deliverables
- Training staff on AI limitations, hallucination risk, and appropriate human oversight practices
- Maintaining an incident response process specifically for AI failures or unexpected outputs in government work
- Documenting how sensitive government data is handled by AI tools — particularly cloud-based AI services with data retention policies
Tier C: Competitive Governance Obligations
These are governance practices that are not yet legally required but are becoming material evaluation factors in competitive procurements. Organizations that can demonstrate mature AI governance will have a measurable advantage over those that cannot.
- Formal AI use policy aligned to NIST AI RMF and DoD AI Ethics Principles
- Named AI governance lead or committee with documented authority and responsibilities
- Published AI use standards governing contractor employees in the performance of government contracts
- AI supplier/vendor assessment process for third-party AI tools used in government work
- Track record of responsible AI deployment demonstrated through past performance narratives
The most acute near-term risk for most federal contractors is the use of AI tools — particularly LLMs for document drafting, code generation, or analysis — in government deliverables without disclosure or validation processes. As agencies implement AI disclosure requirements, contractors discovered to have used undisclosed AI in deliverables face contract performance findings, cure notices, and reputational damage. Every contractor should establish an AI use disclosure and validation process immediately, regardless of formal contract requirements.
Risk-Tiering AI Systems
Not all AI systems present the same governance burden. Effective AI governance requires a risk-tiering methodology that allocates oversight resources proportionately to the risk profile of each AI use case. Both OMB M-24-10 and the NIST AI RMF call for risk-based approaches to AI governance — but they leave the specific tiering methodology to implementing organizations.
The following risk classifier incorporates the key risk dimensions identified in M-24-10 and the NIST AI RMF to produce a tier assignment for a given AI system. Use this tool to assess each AI use case in your organization separately — a single contractor organization may have AI systems spanning multiple tiers.
Risk Tier Definitions
| Tier | Risk Level | Characteristics | Governance Intensity |
|---|---|---|---|
| Tier 1 | Critical | AI directly makes or strongly influences consequential decisions affecting individual rights, safety, security, or mission outcomes. No human review before action. | Maximum — full NIST AI RMF implementation, independent review, DoD AI Assurance framework |
| Tier 2 | High | AI significantly informs consequential decisions or operates autonomously in high-stakes environments. Human oversight is present but may not catch all issues. | Substantial — algorithmic impact assessment, bias evaluation, documented oversight protocols, regular audits |
| Tier 3 | Moderate | AI assists human decision-making in moderate-stakes contexts. Humans have clear authority and realistic ability to review AI outputs before action. | Standard — policy documentation, testing, disclosure, basic monitoring, periodic review |
| Tier 4 | Low | AI used for administrative productivity, research assistance, or internal tasks with no direct consequential decision impact. No government data processed. | Baseline — use policy, staff training, basic disclosure practices for any government deliverables |
OMB M-24-10 specifically identifies "safety-impacting" and "rights-impacting" AI as requiring heightened governance — mandatory human review before AI-informed consequential decisions, algorithmic impact assessments, and annual compliance reporting. Any contractor AI system touching DoD mission readiness, law enforcement, financial eligibility, or benefits determination should be assessed against this heightened standard, regardless of where it appears in your internal tier classification.
The Governance Framework
An effective AI governance framework for a federal contractor is not a policy document — it is an operational structure that makes responsible AI behavior the path of least resistance for every employee and system the organization deploys. This section presents the six-pillar governance architecture that Continuum has developed and applies in its own operations, aligned to the NIST AI RMF's GOVERN function and the DoD AI Ethics Principles.
The Six Governance Pillars
Policy & Documentation Requirements
The documentation backbone of a compliant AI governance program serves two simultaneous functions: it operationalizes governance intent into actionable employee guidance, and it provides the auditable evidence of responsible practice that government customers increasingly expect. The following documents are the minimum viable documentation set for a federal contractor AI governance program.
Core Documentation Suite
| Document | Purpose | Required By | Update Cadence |
|---|---|---|---|
| AI Use Policy | Defines permissible and prohibited AI uses in contract performance; disclosure requirements; validation standards for AI-assisted deliverables | Best Practice / Emerging Contract Req. | Annual + on major policy change |
| AI System Inventory | Registry of all AI systems in use, with risk tier, purpose, data processed, oversight protocol, and responsible owner | OMB M-24-10 (agencies) / Contractor Best Practice | Continuous — updated on adoption/retirement |
| Model Cards / AI Fact Sheets | Per-system documentation of training data, intended use, known limitations, evaluation results, and oversight requirements | NIST AI RMF / DoD AI Assurance | Per system deployment + material updates |
| Algorithmic Impact Assessment (AIA) | Structured assessment of potential disparate impacts, civil liberties implications, and mitigation measures for Tier 1–2 AI systems | OMB M-24-10 Mandatory for Rights/Safety AI | Before deployment + annual for active systems |
| AI Procurement Checklist | Standardized due diligence for evaluating third-party AI tools and services before adoption for government work | Best Practice / Supplier Risk Management | Updated quarterly / on new tool adoption |
| Human Oversight Protocol (per system) | Documents who reviews AI outputs, under what conditions, using what criteria, with what authority to override or reject | M-24-10 / DoD AI Ethics (Governable) | Per system; reviewed annually |
| AI Incident Response Plan | Defines what constitutes an AI incident, detection methods, escalation paths, government customer notification procedures, and remediation steps | Best Practice / Emerging Contract Req. | Annual review + post-incident update |
| Annual AI Governance Report | Summary of AI inventory, governance activities, incidents, testing results, and next-year plan — demonstrable evidence of active governance program | NIST AI RMF / Competitive Best Practice | Annual |
The AI Fact Sheet: Federal Contractor Standard
The Model Card / AI Fact Sheet is the foundational per-system documentation artifact. Drawing on the NIST AI RMF Playbook and DoD AI Assurance framework requirements, Continuum's AI Fact Sheet template includes the following minimum fields for any AI system used in federal contract performance:
Human Oversight & Human-in-the-Loop Design
Human oversight is not a regulatory checkbox — it is the operational mechanism by which AI governance principles are enforced in real time. OMB M-24-10 establishes that federal agencies must ensure "meaningful human oversight" of AI systems making or informing consequential decisions. For contractors, this means designing AI workflows that make human oversight genuinely effective — not merely nominal.
Four Dimensions of Meaningful Oversight
Regulators and auditors assessing human oversight will evaluate four dimensions. Governance programs must address all four:
HITL Design Patterns by Risk Tier
| Risk Tier | Required HITL Pattern | Override Mechanism | Documentation |
|---|---|---|---|
| Tier 1 — Critical | Human decision authority — AI provides information only; no AI action precedes human approval; dual-approval for irreversible actions | Always available; no circumvention possible | Every decision logged with human approver identity and review basis |
| Tier 2 — High | Human confirmation gate — AI recommends; designated reviewer confirms before system acts; exception sampling for routine decisions | Available at any step; system pauses on request | Confirmation events logged; exception sample reviews documented |
| Tier 3 — Moderate | Human review workflow — AI output surfaces to human queue before delivery to government customer or downstream system; exceptions escalated | Available; reviewer can flag for senior review | Review completion logged; escalations tracked |
| Tier 4 — Low | Spot-check monitoring — AI operates with periodic human sampling of outputs; anomalies escalated; AI-generated content in deliverables disclosed | Sampling alerts reviewable | Sampling schedule and results documented; disclosure records maintained |
Testing, Evaluation & Red-Teaming
The NIST AI RMF's MEASURE function requires organizations to assess AI system behavior against established standards and identify deviations. For federal contractors, this translates to a testing regime that goes substantially beyond functional validation — it must address behavioral alignment, adversarial robustness, bias, and the specific failure modes of LLM-based systems.
Testing Dimensions for Federal AI
- Functional Accuracy: Does the system perform its intended function correctly across the intended input distribution? Validated against labeled test sets appropriate to the use case and domain.
- Behavioral Alignment: Does the system behave consistently with the DoD AI Ethics Principles and organizational use policy? Tested through structured scenario evaluation covering edge cases and policy-relevant inputs.
- Bias & Fairness: Does the system produce disparate outputs across protected classes? Required for any system touching rights-impacting or safety-impacting decisions under M-24-10. Uses demographic parity, equalized odds, and counterfactual fairness metrics as appropriate to the decision type.
- Adversarial Robustness: Does the system behave predictably when inputs are adversarially crafted? For LLM-based systems this includes prompt injection testing, jailbreak probing, and data poisoning resistance for RAG-based systems.
- Hallucination Evaluation: For LLM systems, what is the rate of factually incorrect outputs, and under what conditions does it increase? Evaluated using domain-specific factual benchmarks and retrieval accuracy metrics.
- Explainability: Can the system's outputs be explained in terms a human reviewer can use to evaluate correctness? Assessed through structured explainability review with representative users.
Red-Teaming for Federal AI Systems
Red-teaming — adversarial testing conducted by a team attempting to find system failures — is required for Tier 1 systems and strongly recommended for Tier 2 systems under the DoD AI Assurance framework. Continuum's red-team methodology for LLM-based systems in federal contexts includes five attack categories:
| Attack Category | Description | Federal Context Relevance | Tier |
|---|---|---|---|
| Prompt Injection | Malicious instructions in user inputs or retrieved documents redirect model behavior | Critical for RAG systems with document ingestion; could expose classified data or trigger unauthorized actions | 1–2 |
| Jailbreak Probing | Attempts to bypass content policy and safety guardrails through instruction manipulation | Tests whether safety constraints are robust to adversarial users; relevant to any public-facing federal AI | 2–3 |
| Data Poisoning | Injection of malicious content into knowledge bases or training corpora | Critical for RAG systems with open document ingestion pipelines; supply chain risk for fine-tuned models | 1–2 |
| Bias Elicitation | Structured prompting designed to surface latent biases in model outputs | Required for rights-impacting and safety-impacting AI under M-24-10; civil rights compliance | 1–3 |
| Information Extraction | Attempts to recover training data, system prompts, or other sensitive information from model responses | Particularly relevant where model has been fine-tuned on sensitive data; ITAR/CUI protection | 1–2 |
Continuum's LLM Defense Evaluation publication (CR-03) provides the full evaluation framework — covering quality assurance, fairness, security, and mission-appropriateness criteria — that informs our testing practice for defense AI systems. The framework is directly applicable to meeting the DoD AI Assurance requirements and the NIST AI RMF MEASURE function for Tier 1 and Tier 2 systems.
AI Procurement Governance
Federal contractors are not only responsible for the AI they build — they are responsible for the AI they buy, lease, or use through API access. Third-party AI tools and services used in government contract performance carry the same governance obligations as internally developed systems. This is the most underappreciated governance gap in the current federal contractor community.
Third-Party AI Due Diligence Requirements
Before any third-party AI tool is approved for use in government contract performance, contractors should complete the following due diligence:
- Confirm the tool does not transmit government data, CUI, or contract-sensitive information to provider servers in ways that violate data handling obligations
- Obtain and review the provider's AI governance documentation — terms of service, privacy policy, data retention policy, and any published model cards or AI use documentation
- Determine whether the tool has received any federal authorization (FedRAMP, DoD IL certifications) appropriate to the classification level of the work
- Assess the provider's testing and safety practices against the evaluation dimensions in Section 08
- Verify that the tool's outputs can be attributed and reviewed as required by your human oversight protocol for the applicable risk tier
- Confirm the tool does not create IP ownership complications for deliverables it assists in generating
- Assess the provider's incident notification obligations — will they inform you if a security event occurs that may have affected government data?
- Do not use general-purpose consumer AI tools (free tiers, non-enterprise plans) for any work involving government data, CUI, or contract-sensitive information
- Do not rely on provider marketing claims of "FedRAMP authorization" without verifying the specific service and authorization level on the FedRAMP marketplace
- Do not assume that using an approved cloud provider automatically makes all AI services on that platform compliant — authorization is service-specific
- Do not allow individual employees to independently adopt AI tools for government work without organizational review and approval
AI Tool Approval Process
Continuum recommends a structured four-gate approval process for any AI tool adoption for government work:
Interactive Compliance Checklist
The following checklist synthesizes the requirements, obligations, and best practices from this paper into a trackable compliance program. Click each item to mark it complete or N/A. Use this as a starting point for a gap assessment — not as a substitute for a full legal and technical review with your counsel and governance team.
Governance Maturity Assessment
The NIST AI RMF positions AI governance as a maturity journey, not a binary compliance state. The following interactive maturity model allows organizations to self-assess their current AI governance maturity across six domains, identify gaps, and prioritize investments. Rate each capability area from 1 (Initial/Ad Hoc) to 4 (Optimizing) by clicking the dots.
Implementation Roadmap
Building a compliant federal contractor AI governance program does not require a transformation initiative. It requires a structured, phased approach that establishes the minimum viable governance structure quickly, then matures it systematically. The following 12-month roadmap reflects the implementation sequence Continuum recommends based on contractor size, current maturity, and the urgency of specific compliance obligations.
The Continuum Approach
Continuum Resources does not offer AI governance as an advisory service divorced from technical practice. We live the governance requirements we write about. Our own AI deployments — for Space Force, Navy, financial institutions, and educational organizations — are built and operated within the governance framework described in this paper, aligned to the NIST AI RMF, the DoD AI Ethics Principles, and OMB M-24-10.
This means that when we help a client build an AI governance program, we are not working from theory — we are sharing an operational program that has been tested against real DoD program office scrutiny, real financial regulatory environments, and real competitive procurement evaluations. The difference is significant.
- Governance Program Build: End-to-end development of a federal contractor AI governance program — from charter to compliance checklist to staff training — aligned to NIST AI RMF, M-24-10, and DoD AI Ethics Principles. Delivered in 60–90 days for most organizations.
- AI System Inventory & Risk Assessment: Independent assessment of all AI tools in use, risk tier assignment, gap identification against applicable governance requirements, and prioritized remediation roadmap.
- AI Fact Sheet Development: Documentation of all AI systems in use, including technical description, evaluation results, oversight protocols, and compliance status — audit-ready for government customer review.
- Red-Team & Evaluation Services: Adversarial testing of AI systems against the five attack categories, bias evaluation, hallucination benchmarking, and alignment assessment. Produces the evaluation documentation required for Tier 1–2 systems under the DoD AI Assurance framework.
- Proposal Integration: Development of AI governance past performance narratives, capability statements, and compliance documentation for inclusion in federal proposals where AI governance is an evaluation factor.
- Published Research Foundation: Our LLM Defense Evaluation (CR-03) and Secure RAG Architectures (CR-04) publications provide the peer-reviewed technical foundation for our governance and evaluation practice — not marketing content, but operational research applied directly to client programs.
Why Continuum for AI Governance
| Differentiator | What It Means for You |
|---|---|
| PhD-Level R&D Leadership | Kurt A. Richardson, PhD brings academic rigor to governance practice. Our frameworks are defensible under expert scrutiny — not consultant boilerplate. |
| Active DoD Program Experience | We govern AI in active Space Force, Navy, and Army programs. We know what program offices actually look for, not what policy documents say they should look for. |
| Published Research Basis | Our four published research papers directly inform our governance practice. When we cite an evaluation standard, we wrote the standard — and it has been peer-reviewed. |
| WOSB & SBA Certified | As a certified WOSB, we can serve as a subcontractor to large primes seeking to meet small business goals while adding high-quality AI governance capability to their programs. |
| End-to-End Delivery | AI governance + technical AI deployment + DevSecOps + Agile + Testing. We govern the systems we build. No gap between the governance framework and the technical reality. |
Conclusion
The federal AI governance environment has moved from aspiration to obligation with remarkable speed. The policy architecture now in place — OMB M-24-10, EO 14110, the DoD AI Ethics Principles, and the NIST AI RMF — creates real governance obligations for federal contractors that will only intensify as FAR/DFARS rulemaking catches up to executive policy and as program offices become more sophisticated in their AI oversight expectations.
The contractors who recognize this shift and build compliant governance structures today will compete more effectively, deliver more responsibly, and maintain the trust of their government customers as AI becomes an increasingly central element of contract performance. Those who wait for a specific contract clause to force action will find themselves behind competitors who treated governance as a strategic investment rather than a compliance cost.
The framework, tools, and roadmap presented in this paper represent a practical, implementable starting point — not a theoretical aspiration. For a 30-person specialized contractor, minimum viable governance can be established in 30 days. For a 500-person prime with multiple program lines, a robust governance program can be operational within 90 days. The investment is proportionate to the obligation — and the obligation is no longer optional.
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- [CR-03] Richardson, K.A. — "LLM Defense Evaluation" — Continuum Resources, 2024. Defense-focused evaluation framework for open-source LLMs; directly applied in Section 08 testing protocols.
- [CR-04] Richardson, K.A. — "Secure RAG Architectures" — Continuum Resources, 2024. RAG security design patterns referenced in Sections 08 and 09 for data governance and adversarial robustness.
- [EO-14110] Executive Order 14110 — "Safe, Secure, and Trustworthy Artificial Intelligence" — White House, October 30, 2023.
- [OMB-M-24-10] OMB Memorandum M-24-10 — "Advancing Governance, Innovation, and Risk Management for Agency Use of Artificial Intelligence" — Office of Management and Budget, March 28, 2024.
- [NIST-AI-RMF] National Institute of Standards and Technology — "Artificial Intelligence Risk Management Framework (AI RMF 1.0)" — NIST AI 100-1, January 2023.
- [NIST-PLAY] National Institute of Standards and Technology — "AI RMF Playbook" — NIST, 2023. Implementation guidance for the GOVERN, MAP, MEASURE, and MANAGE functions.
- [DoD-AI-ETHICS] Department of Defense — "DoD AI Ethics Principles" — Office of the Chief Digital and Artificial Intelligence Officer, February 2020.
- [DoD-ADOPT] Department of Defense — "DoD AI Adoption Strategy" — CDAO, June 2023.
- [DoD-RAI] Department of Defense — "Responsible AI Guidelines in Practice" — CDAO, 2023. Operational guidance for implementing the DoD AI Ethics Principles.
- [EO-13960] Executive Order 13960 — "Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government" — White House, December 2020.
- [FAR] Federal Acquisition Regulation — Current edition. Relevant subparts: FAR 9 (Contractor Qualifications), FAR 52 (Contract Clauses). Monitoring for AI-specific rulemaking.
- [DFARS] Defense Federal Acquisition Regulation Supplement — Current edition. Monitoring for CDAO-directed AI governance clause additions.
- [NIST-SP-800-53] National Institute of Standards and Technology — "Security and Privacy Controls for Information Systems and Organizations," SP 800-53 Rev 5, 2020. Baseline security controls applicable to AI system deployment in federal environments.