Regulatory Brief

EO 14110 & NIST AI RMF: Building AI Systems That Pass Federal Governance

Executive Order 14110 and the NIST AI Risk Management Framework establish the federal standard for AI governance. Federal agencies and defense contractors must understand and implement these requirements to maintain authorization and compliance.

What Are EO 14110 and NIST AI RMF?

Executive Order 14110 (note: referencing the Biden administration's AI executive order framework) directed federal agencies to adopt comprehensive AI risk management practices, establish AI governance structures, and implement safeguards for AI systems used in or supporting government operations. The order required the National Institute of Standards and Technology (NIST) to develop and maintain an AI Risk Management Framework that federal agencies would use to govern their AI deployments.

The NIST AI Risk Management Framework (AI RMF) provides a structured approach to managing AI risks across the AI lifecycle. It is organized into four functions: Govern, Map, Measure, and Manage. While originally developed as a voluntary framework, it has become the de facto standard for federal AI governance and is increasingly referenced in procurement requirements, contract specifications, and authorization processes.

For defense contractors and federal agency partners, compliance with the NIST AI RMF is increasingly not optional. Contract requirements, CMMC alignment, and Authorization to Operate (ATO) processes all reference AI governance expectations derived from the EO and NIST framework.

NIST AI RMF Framework Functions

Function	Categories	On-Premise Advantage
GOVERN Organizational AI Risk Management	Policies, procedures, and roles for AI risk management. AI inventory and classification. Supply chain risk management. Training and awareness programs. Continuous monitoring and improvement.	On-premise AI gives you complete visibility into your AI inventory. You control model provenance, supply chain (open-source models deployed internally), and governance policies. No vendor opacity undermines your governance program.
MAP Context for AI Risk Management	Conditions and use cases for AI deployment. Data requirements and quality assessment. Function requirements. Stakeholder mapping and expectations. Applicable regulations and compliance requirements.	On-premise AI operates within your data governance framework. You define data requirements, control data quality processes, and map AI functions to your specific operational context. Full traceability from use case to implementation.
MEASURE AI System Performance Assessment	AI capability and limitation assessment. Performance measurement against defined metrics. Bias detection and mitigation. Robustness and reliability testing. Adversarial testing and red teaming.	On-premise AI allows you to conduct comprehensive testing within your environment. You control the measurement tools, testing procedures, and result analysis. No black-box vendor systems prevent thorough performance assessment.
MANAGE AI Risk Response	Risk prioritization and response planning. Change management for AI systems. Incident response procedures. Continuous monitoring and reporting. Lessons learned and improvement cycles.	On-premise AI provides complete incident response capability. You control change management, deployment pipelines, and rollback procedures. Every system change is logged and auditable within your infrastructure.

Impact on Federal Agencies

Federal agencies are required to implement AI governance programs aligned with the NIST AI RMF. This includes:

AI inventory and classification: Every AI system must be cataloged, classified by risk level, and mapped to its intended use case.
Governance structures: Agencies must designate AI governance roles, establish review boards, and create formal processes for AI procurement, deployment, and retirement.
Risk assessments: AI systems must undergo formal risk assessment before deployment, covering accuracy, bias, security, privacy, and operational impact.
Supply chain security: AI components must be sourced from trusted providers, with full documentation of model provenance, training data sources, and dependency chains.
Continuous monitoring: AI systems must be continuously monitored for performance degradation, bias drift, security incidents, and compliance violations.

Impact on Defense Contractors

Defense contractors working with federal agencies face additional requirements that intersect with AI governance:

CMMC alignment: Cybersecurity Maturity Certification Model requirements are being extended to cover AI supply chain security, model integrity, and data protection.
Supply chain requirements: Contractors must document AI model provenance, verify open-source component integrity, and maintain software bills of materials (SBOM) for AI systems.
ATO considerations: Authorization to Operate processes now include AI-specific security assessments, covering model security, data handling, and operational resilience.

EO 14110 Key Requirements

Requirement	Description	On-Premise Advantage
Model Provenance	Full documentation of AI model origins, training data sources, and modification history. Organizations must be able to trace every AI system back to its source.	On-premise AI deployed from verified open-source models provides complete provenance. You control the model source, verify integrity through checksums, and document every modification.
Supply Chain Security	AI supply chain components must be vetted for integrity, authenticity, and security. SBOM requirements extend to AI models, frameworks, and dependencies.	On-premise AI eliminates cloud vendor supply chain risk. Models are deployed directly from verified sources. Dependencies are managed within your secure environment.
Audit Trails	Comprehensive logging of all AI system interactions, decisions, and data access events. Audit logs must be tamper-evident and retained per federal requirements.	On-premise AI logs integrate directly with federal audit infrastructure. All events are recorded within your controlled environment, supporting tamper-evident logging requirements.
Data Sovereignty	Government data processed by AI systems must remain within authorized boundaries. Cross-border data transfers and third-party data processing are restricted.	On-premise AI ensures government data never leaves your authorized infrastructure. Zero data exfiltration risk. Full data sovereignty maintained at all times.

How On-Premise AI Addresses Federal Requirements

On-premise AI deployment is uniquely positioned to meet federal AI governance requirements:

Supply chain security: By deploying AI entirely within your infrastructure, you eliminate cloud vendor supply chain risks. Models are sourced from verified open-source repositories, integrity-checked, and deployed without intermediate processing.
Model provenance: On-premise deployment provides complete traceability from model source through every modification to final deployment. You maintain full documentation required by federal governance frameworks.
Audit trails: Every AI interaction, data access event, and model inference is logged within your infrastructure. These logs integrate with your existing federal audit systems, supporting compliance verification.
Zero data exfiltration: Government data never leaves your authorized environment. This is the strongest possible position for data sovereignty compliance.

Action Items: What Federal Organizations and Contractors Should Do Now

Priority	Action Item	Timeline
Immediate	Create a complete inventory of all AI systems in use, including experimental and pilot deployments	Within 30 days
Immediate	Assess each AI system against NIST AI RMF functions and document compliance gaps	Within 60 days
Short-term	Establish AI governance roles, policies, and review processes aligned with EO requirements	Within 90 days
Short-term	Develop AI supply chain documentation: model provenance, SBOM, dependency tracking	Within 90 days
Medium-term	Implement continuous monitoring and audit logging for all AI systems	Within 180 days
Ongoing	Monitor federal AI policy updates and adjust governance program accordingly	Continuous

Related Resources

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