CMMC is evolving. Defense contractors face a critical new challenge: an AI Framework that will reshape compliance requirements. The Department of Defense is adding AI-specific security controls to CMMC 2.0, so organizations seeking CMMC certification now deal with additional layers of complexity. This framework ties to the National Defense Authorization Act (NDAA) and especially affects contractors using AI tools or machine learning systems in their operations. You need to understand these AI Framework for CMMC requirements now because implementation timelines are approaching. We’ll break down what you need to know about CMMC compliance under this new framework and how to prepare your organization.
Understanding the New AI Framework Requirements
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Understanding the New AI Framework Requirements
What the Framework Covers
The National Defense Authorization Act for Fiscal Year 2026 directs the Department of Defense to develop a framework that addresses both cybersecurity and physical security of artificial intelligence and machine learning technologies the Pentagon acquires. This framework extends beyond traditional cybersecurity measures to cover the complete security posture of AI systems used in defense operations.
The scope applies to “covered AI/ML,” which has AI and machine learning acquired by DoW along with all associated components: source code, model weights, methods, algorithms, data, and software used to develop the AI/ML. Your organization must follow the framework if it touches any part of the AI development or deployment pipeline for DoW contracts.
The framework targets AI-specific security risks that traditional cybersecurity controls don’t address well. Data poisoning occurs when attackers contaminate the datasets on which ML models train. This causes them to misclassify information, generate biased output, or embed hidden vulnerabilities. Adversarial tampering involves intentional compromise of hardware, software, data, or processes. Unintentional data exposure happens when sensitive data gets disclosed accidentally through mistakes in configuration, handling, access controls, or processes.
The NDAA states that the framework must be implemented as an extension or increase of existing DoW cybersecurity frameworks and CMMC. Contractors won’t face a separate compliance regime but rather boosted requirements layered onto current CMMC obligations.
The framework will guide specific security measures that contractors must adopt. These measures are tailored to the particular AI/ML technologies and tasks they handle. Different AI applications will require different security controls based on their risk profiles and operational contexts.
Who Must Comply
The security requirements apply to “covered entities,” defined as entities entering into contracts or agreements with the DoW for the development, deployment, storage, or hosting of covered AI/ML. This definition captures a broad range of contractors across the defense industrial base.
Your organization must comply if it develops AI models for DoW applications. You must comply if you deploy AI systems in DoW environments. You must comply if you store AI model data or host AI services for DoW operations. The framework reaches any point where AI/ML intersects with DoW contracts.
Organizations already subject to CMMC Level 2 requirements face additional obligations when AI enters their work scope. The 110 security practices from NIST SP 800-171 required for CMMC Level 2 remain in place, with AI-specific controls added on top. Subcontractors handling AI/ML for prime contractors also fall under these requirements and extend compliance obligations throughout the supply chain.
Connection to National Defense Authorization Act
Section 1513 of the NDAA for Fiscal Year 2026 directs the DoW to incorporate the AI framework into both the Defense Federal Acquisition Regulation Supplement and the CMMC program. The DFARS will be amended to mandate that DoW contractors implement the framework’s best practices once developed.
The NDAA requires DoW to conduct a cost-benefit exercise that weighs the benefits of imposing new security requirements against the costs of slowing down AI/ML development and deployment. This analysis recognizes that overly burdensome requirements could stifle state-of-the-art work while inadequate controls create unacceptable risks.
Section 1513 does not provide an implementation deadline for the framework or security requirements. DoW must create a plan that establishes implementation timelines and milestones and provide a status update to Congress by June 16, 2026. This status update will clarify when contractors must begin meeting the new requirements.
The framework arranges itself with broader NDAA provisions on AI governance. Section 6602 requires heads of intelligence agencies to track and review performance of procured and agency-developed artificial intelligence. This covers efficacy, safety, fairness, transparency, accountability, appropriateness, lawfulness, and trustworthiness. These criteria signal the security dimensions DoW contractors will need to address in their AI implementations.
Breaking Down the AI Framework Components
“The framework will focus on highly capable AI systems that may be of highest interest to cyber threat actors, applying stringent security requirements that align with protections for national security systems.” — U.S. Department of Defense, Federal regulatory authority responsible for national security cybersecurity standards
Five core components are the foundations of the AI Framework for CMMC. Each one needs specific implementation steps that extend your current compliance efforts. Defense contractors must address these components in a systematic way to meet DoW expectations.
AI Model Inventory and Classification
The Cloud Security Alliance AI Controls Matrix provides a structured approach. It has 243 control objectives across 18 domains designed for AI security and governance. The AICM offers useful controls for organizations operating in a CMMC Level 2 environment. These controls map to NIST SP 800-171 requirements, which has Identity & Access Management, Data Security & Privacy, Audit Logging, and Model Security.
Model cards and data sheets serve as documentation tools for AI systems. The CSA AI Model Risk Management Framework introduces practical tools to document and mitigate model risks. These tools are model cards, data sheets, and risk scenario planning. Model cards provide evidence of model provenance and intended use. Risk cards help document known vulnerabilities and incident responses. This documentation is a great way to get value during CMMC assessments because it demonstrates proactive management of AI risks.
Risk Assessment Requirements
Risk assessment for AI systems needs continuous evaluation rather than periodic checks. The NIST AI Risk Management Framework outlines a lifecycle approach. It has four key phases: Map, Measure, Manage, and Govern. NIST provides the overarching process and CSA delivers detailed control checkpoints.
Organizations must identify data governance risks. AI systems that access, process, or store CUI without proper controls create these risks. Machine learning models need training data. That data may include CUI, and you’ve expanded your compliance boundary. Every dataset, training environment, and model output becomes subject to CMMC controls.
Operational risks surface. AI-generated outputs that inform mission-critical decisions without human oversight create them. CMMC Level 2 demands accountability for all actions with CUI. Predictive analytics can forecast emerging threats and anticipate areas of non-compliance before they show up as security incidents.
Security Control Implementations
Access Control (3.1.x) requirements determine who can use AI systems. They also determine whether access rights are limited based on role and need. You must demonstrate that users who query AI with CUI have appropriate clearance and authorization. This extends to the vendor’s access to your data for cloud AI tools.
Audit and Accountability (3.3.x) mandates logging all AI interactions. This means prompts submitted and responses generated. You must retain logs for the required period and protect their integrity. Identification and Authentication (3.5.x) needs multi-factor authentication and integration with your identity provider.
System and Communications Protection (3.13.x) will give all data encryption in transit and at rest. Network communications must be monitored. AI systems should reside on segmented networks appropriate for CUI. System and Information Integrity (3.14.x) needs monitoring AI systems for security-relevant events and screening inputs and outputs for malicious content.
Documentation and Reporting Standards
Your System Security Plan must specify the role of AI tools, how they are secured, and their scope and boundary. Natural language processing capabilities can transform technical data into comprehensible narratives for multiple stakeholders. Automated documentation reduces human error and enables quick compilation of compliance evidence.
Plans of Action and Milestones detail remediation paths for unimplemented controls. These paths have resources, milestones, and assigned owners. CMMC needs these closed within 180 days.
Third-Party AI Tool Considerations
Any AI component that stores, processes, or transmits CUI must be FedRAMP Authorized or FedRAMP Moderate equivalent. Using unvetted commercial cloud AI to handle CUI evidence violates DFARS 252.204-7012 and CMMC Program Rule requirements. Organizations must use only AI tools and platforms that have FedRAMP Moderate Authorization or equivalency.
How This Changes Your CMMC Compliance Strategy
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AI integration forces defense contractors to rethink their whole CMMC compliance approach. The changes extend beyond adding new controls to alter how assessments are scoped, documented, and managed to keep them current.
Updates to CMMC 2.0 Assessment Process
Your CMMC Assessment Scope must now include AI systems within the boundary definition before assessment begins. AI tools that process, store, or transmit CUI become part of the assessed environment. Certified Assessors will review information and evidence to confirm that you meet stated assessment objectives for all requirements, including those related to AI usage.
Contract criticality determines the assessment type. Prioritized acquisitions just need a triennial C3PAO assessment with results posted to SPRS. Non-prioritized acquisitions allow annual self-assessments by your senior official, submitted to SPRS. AI systems often support prioritized programs, which means more contractors will face third-party assessments rather than self-assessments.
You may receive Plans of Action and Milestones for controls not implemented after the original assessment. POA&M closeout assessments review only the NOT MET requirements identified during the original review. AI-related POA&Ms must demonstrate remediation within the required timeframe and prove that AI security gaps have been closed.
Assessment evidence collection moves from periodic snapshots to continuous confirmation. Traditional compliance workflows built around annual reviews fail to catch control drift early enough. Contractors must keep assessment-ready evidence year-round, not just before scheduled evaluations.
New Documentation Requirements
Your System Security Plan requires substantial updates when AI enters your environment. The SSP must document any AI systems within your assessment boundary and describe how they process CUI, what controls apply, and how you monitor them. Assessors need sufficient detail to confirm control implementation.
Policies and procedures demand coverage of AI. Update acceptable use policies to address AI tools and create procedures for approved AI usage. Detail prohibited activities and define incident response steps if CUI is exposed. These policies are the foundations of governance that assessors will review.
Assessment evidence must demonstrate AI tools operate in compliant environments. Keep configuration documentation and capture access controls. Create data flow diagrams and document monitoring capabilities. Risk assessments must include AI systems and document risks, mitigations, and ongoing monitoring. Annual CMMC affirmations require you to confirm no changes have compromised compliance, which means documenting how AI implementations keep your security posture intact.
Book a Readiness Call to discuss how these documentation changes affect your specific environment and develop a systematic approach to meeting these expanded requirements.
Additional Security Controls to Implement
Incident response plans need AI-specific scenarios. Prepare playbooks for AI data spillage and model misuse or compromise events. Define how you’ll respond if an employee leaks CUI to an external AI system. Update incident response plans to include detection, containment, and remediation steps specific to AI risks.
Continuous monitoring extends to AI activities. Use data loss prevention tools and Cloud Access Security Brokers to detect and block unauthorized AI usage, such as attempts to send data to external AI services. Review logs from approved AI systems to catch disallowed prompts or disclosure of internal data.
Access governance becomes more complex. You need cross-functional review before implementing AI use cases and must keep a catalog of vetted AI tools with clear restrictions and required configurations. Review approved tools quarterly to ensure ongoing compliance. AI capabilities evolve faster, and vendors change data handling practices, which requires continuous reassessment rather than set-and-forget configurations.
AI-Specific Security Controls You Must Implement
Defense contractors implementing AI within CMMC environments must deploy four categories of technical security controls that address vulnerabilities unique to artificial intelligence systems. Traditional cybersecurity measures cannot address these risks.
Data Input Validation and Sanitization
Prompt injection represents the most prominent AI-specific security concern. Attackers craft inputs designed to make the model ignore its instructions and perform unauthorized actions. A successful prompt injection could extract sensitive information from the model’s context window in a CUI environment.
Input sanitization strips known prompt injection patterns before they reach your AI models. Preprocessing techniques like feature squeezing and input transformation flag or filter suspicious content. The goal is to ensure that only well-vetted data gets processed by the AI system, and this minimizes the attack surface available to adversaries.
Output filtering prevents the model from returning raw CUI in unexpected formats. Rate limiting restricts the number of queries from a single source within a defined timeframe and prevents automated attacks. Monitoring flags unusual query patterns for security review and creates an additional detection layer.
Model Access Controls and Authentication
The AI system must authenticate users through your existing directory service. This avoids the complexity of managing federated authentication with a cloud provider and ensures consistent identity governance. Apply role-based access arranged to least privilege principles. Not every user needs access to every model or AI capability.
Access control at the model level allows you to gate-keep based on cost, sensitivity and necessity. Multi-factor authentication and conditional access policies are the foundations for user authentication. Role-based access control frameworks must scale in a variety of AI platforms, with granular permission controls that have model-specific access rights and data classification-based permissions.
Tool-level access control defines which tools an agent can call. Session management requires timeout policies and concurrent session limits. Live behavioral analytics detect anomalous access patterns. Permissions should be tied to roles rather than individual users, and this ensures clear boundaries between administrators, developers and end users.
AI Output Monitoring and Logging
All interactions with the AI system are logged to your SIEM. This has who submitted each query, what data was included, what response was generated and when the interaction occurred. These logs support both the audit logging requirements of NIST 800-171 and the incident response capability that CMMC assessors will assess.
Log all AI interactions in environments handling CUI. Your SIEM monitoring and security operations center oversight should have this data. Every model invocation can be logged with full context and creates a centralized audit trail critical for compliance and forensic analysis.
Ensure the AI system cannot exfiltrate data through its outputs. The AI endpoint should have no outbound network connectivity. Output size limits prevent bulk data extraction. Data loss prevention tools should monitor AI outputs just as they monitor email and file transfers.
Adversarial Attack Prevention Measures
Adversarial attacks exploit vulnerabilities in AI systems through crafted inputs. Data poisoning occurs when attackers contaminate the datasets on which ML models train to misclassify information or generate biased output. Evasion attacks occur after the model is deployed and involve modified input data to trick the trained model into making wrong classifications.
Continuous model validation tracks metrics like accuracy and error rates. Anomalies in model outputs ensure you don’t miss warning signs that the AI model has been compromised. Techniques such as anomaly detection and defensive distillation help identify when the model’s behavior deviates from expected performance and potentially signal an ongoing attack.
Conduct adversarial testing on all AI models by challenging them with inputs designed to expose weaknesses. Monitor the AI’s outputs for anomalies that could show tampering, such as sudden output of gibberish that might hint at a corrupted model. Update models with security patches or improved versions to fix vulnerabilities on a regular basis.
Getting Your Team Ready for AI Framework Compliance
Building a compliant AI program requires more than technical controls. The cybersecurity talent gap remains the most important obstacle for many organizations, as you need highly skilled professionals to interpret complex data analytics, configure sophisticated security controls, and manage compliance frameworks. Organizations forced to operate with lean teams find it difficult to keep pace with evolving CMMC requirements, especially when you have AI-specific security demands added.
Required Training and Certifications
Security awareness training must extend to include AI-specific risks. Users need training on how to identify CUI and FCI, along with the rule that this data must not be shared with unauthorized systems. Humans are poor at spotting what qualifies as CUI consistently, so clear examples and data labeling tools help users make correct decisions.
Training should cover proper use of approved AI tools. This includes interface navigation, acceptable input data types, and how outputs should be handled. If outputs contain CUI, users must know how to mark and store them the same way they would an email containing CUI. The dangers of Shadow AI need attention in training programs. Employees must understand that using free AI services or browser extensions for work content violates policy and creates risks.
Incident reporting procedures need emphasis. Staff should report immediately if they pasted something sensitive into an external AI by accident or notice any AI system behaving oddly. Quick reporting allows the security team to perform damage control before situations escalate, such as requesting data deletion from a vendor.
The CMMC Certified Professional certification serves as the foundational credential for professionals working within the CMMC implementation and assessment ecosystem. A CCP confirms readiness to help organizations achieve assessment-ready cybersecurity programs or participate on assessment teams during official CMMC assessments. The exam covers six domains: CMMC Ecosystem, Ethics, Governance and Source Documents, Model Construct and Implementation Evaluation, Assessment Process, and Scoping.
Building Internal AI Security Expertise
Cross-functional collaboration proves essential for AI Framework compliance. Establish an AI Adoption Committee that includes IT, security, compliance, and business units to review AI use cases against CMMC requirements before implementation. This committee reviews proposed AI tools and assesses risks to determine whether implementations can proceed within compliance boundaries.
Invest in training programs that enable staff to use AI tools while promoting collaboration between cybersecurity, IT, compliance, and business units. Regular briefings and cross-departmental workshops can dispel uncertainties associated with AI adoption. Encourage teams to assess AI system performance critically and refine processes.
Working with CMMC Assessment Organizations
The C3PAO will ask about AI tools during your CMMC assessment if they appear in your SSP, interviews, or system inventory. Be prepared to identify every AI tool in use throughout the organization and demonstrate that CUI cannot flow to AI tools outside the boundary through technical controls. You must provide audit logs showing AI system access and usage and show your policy on AI tool usage. The most common finding related to AI tools is undocumented usage, where employees use services for CUI-related tasks without the organization’s knowledge or documentation.
Timeline and Enforcement Dates
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“Section 1513 does not provide an implementation deadline for the framework or security requirements but instructs the DoD to create a plan establishing implementation timelines and milestones and to provide a status update to Congress by June 16, 2026.” — U.S. Department of Defense, Federal regulatory authority responsible for NDAA implementation
When Requirements Take Effect
The AI Framework timeline operates on a different track than CMMC 2.0 implementation. CMMC requirements began appearing in DoW contracts on November 10, 2025, but the AI Framework lacks a specific implementation deadline. Section 1513 instructs the DoW to create a plan that establishes implementation timelines and milestones. Congress expects a status update by June 16, 2026.
This creates a critical difference: CMMC 2.0 is enforceable now, but AI-specific framework requirements remain under development. Contractors must monitor DoW announcements after the June 2026 status update to understand when AI Framework obligations will be incorporated into contract language.
Phased Implementation Approach
CMMC 2.0 follows a four-phase rollout spanning three years and uses a “crawl, walk, run” approach. Each phase introduces progressively stricter certification requirements:
Phase 1 (November 10, 2025 – November 9, 2026) focuses on self-assessments. Level 1 or Level 2 self-assessments are required in applicable solicitations. DoW retains discretion to require Level 2 C3PAO certification for certain contracts even during Phase 1.
Phase 2 begins November 10, 2026 and mandates third-party C3PAO assessments for Level 2 certifications in most contracts handling CUI. Phase 3 starts November 10, 2027 and introduces Level 3 DIBCAC certifications for the most sensitive CUI. Phase 4 reaches full implementation by November 10, 2028. CMMC requirements become mandatory across all applicable DoD solicitations and contracts at that point.
The AI Framework will probably follow a similar phased approach once finalized, given the DoW’s pattern with CMMC rollout.
Grace Periods and Transition Support
No official grace periods exist within the CMMC program. The phased rollout itself serves as the transition mechanism and provides contractors time to achieve compliance without formal extensions. Contractors who demonstrate substantial compliance efforts may receive limited flexibility on lower-priority contracts, but this remains at DoW’s discretion rather than guaranteed relief.
Plans of Action and Milestones offer conditional certification for 180 days and allow contractors to address non-critical control gaps. POA&Ms are restricted to Levels 2 and 3. Failure to close them within the timeframe results in automatic termination of CMMC status.
Common Pitfalls and How to Avoid Them
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Defense contractors pursuing AI Framework compliance repeatedly encounter four critical mistakes that delay certification and create security gaps. These patterns help organizations avoid expensive remediation cycles.
Underestimating Implementation Time
CMMC 2.0 implementation requires 6 to 18 months. Your current cybersecurity posture determines this. Organizations starting from scratch just need 12 to 18 months to build policies, deploy technical controls and train staff. AI Framework requirements extend these timelines further. AI-specific controls demand specialized expertise and testing that traditional security implementations don’t require.
Ignoring AI Shadow IT
Employees install Chrome extensions, try AI tools or sign up for cloud services without IT approval. This Shadow IT 2.0 creates a compliance nightmare. CMMC mandates that users are trained and systems are authorized. Organizations with high shadow AI usage experience breach costs averaging $4.63 million, which is $670,000 more per breach than those with low or no usage. Thirty-seven percent of organizations have already adjusted their security strategies due to AI-driven threats.
Incomplete AI Inventory
The most common finding related to AI tools is undocumented usage. Employees use services for CUI-related tasks without the organization’s knowledge or documentation. Visibility across AI implementations of all types is critical to demonstrate compliance during C3PAO assessments.
Insufficient Testing and Validation
Contractors who schedule official CMMC audits without performing internal mock assessments encounter unexpected findings. Readiness reviews identify compliance gaps before the official audit and prevent expensive failures and remediation delays.
Next Steps for Defense Contractors
An AI inventory is your starting point if you’re preparing for CMMC assessment while using AI tools. Catalog every AI tool in use. This includes browser extensions, Copilot features and personal accounts. Classify which tools interact with CUI, FCI or neither. Assess whether each CUI-adjacent tool meets all applicable NIST 800-171 controls. Non-compliant tools must be removed from CUI workflows. Technical controls should prevent unauthorized usage. Your SSP, policies and procedures need updates to reflect your AI tool posture. Private AI solutions for CUI-related needs are the compliant alternatives you should deploy.
Immediate Actions to Take Now
An AI risk assessment is your first step. Identify all AI tools used or under consideration and map their data flows. Then assess compliance with NIST 800-171 requirements. AI-enabled environments need segregation to prevent CUI from crossing into unapproved systems. Isolated, CMMC-compliant enclaves should house sensitive data operations. A Readiness Call will help assess your current posture and develop a structured roadmap tailored to your organization’s AI usage patterns.
Creating Your Implementation Plan
A 30-day compliance plan works best. Week 1 focuses on inventory and CUI exposure assessment. Week 2 establishes governance frameworks and policies. Week 3 assesses compliant solutions, and Week 4 pilots implementation with full documentation.
Budgeting for Compliance Costs
Level 2 compliance typically requires $100,000-$200,000. First-year expenditures range from $70,000-$250,000. This depends on organizational size and security maturity. Costs should be spread over multiple budget cycles rather than rushed implementation at premium consulting rates.
Conclusion
The AI Framework represents the most important development in CMMC compliance, not an optional addition. Defense contractors using artificial intelligence must act now. Implementation timelines approach faster despite current uncertainties. We’ve covered the key parts: AI-specific security controls, expanded documentation requirements, and modified assessment processes that will reshape how C3PAOs assess your environments.
Start your compliance work by inventorying AI tools. Segregate CUI workflows and update your System Security Plan. Organizations that treat this framework as an afterthought will face remediation cycles that get pricey and contract delays. Proactive preparation prevents compliance failures tomorrow. Book your readiness assessment and build a structured implementation roadmap before enforcement deadlines arrive.
Key Takeaways
Defense contractors face a new compliance reality as the DoD integrates AI-specific security requirements into CMMC 2.0, fundamentally changing how organizations must approach cybersecurity certification.
• AI Framework expands CMMC scope beyond traditional cybersecurity – Any AI tool processing CUI must meet NIST 800-171 controls plus new AI-specific requirements for data validation, access controls, and adversarial attack prevention.
• Immediate inventory and risk assessment are critical – Catalog all AI tools including browser extensions and personal accounts, classify CUI exposure, and remove non-compliant tools from sensitive workflows before assessments.
• Documentation requirements significantly expand – System Security Plans must detail AI implementations, policies need AI-specific coverage, and continuous monitoring replaces periodic compliance checks.
• Implementation timelines are approaching despite uncertainties – While specific deadlines await DoD’s June 2026 status update, CMMC 2.0 enforcement began November 2025, requiring proactive preparation now.
• Shadow AI poses the biggest compliance risk – Undocumented AI usage represents the most common audit finding, with organizations needing technical controls to prevent unauthorized CUI exposure to external AI services.
The convergence of AI capabilities and defense contracting demands immediate action. Organizations that begin compliance preparation now will avoid costly remediation cycles and contract delays when enforcement deadlines are finalized.
FAQs
Q1. What is the AI Framework for CMMC and who needs to comply with it? The AI Framework is a new set of security requirements being added to CMMC 2.0 that specifically addresses artificial intelligence and machine learning systems used by defense contractors. Any organization entering into DoD contracts for the development, deployment, storage, or hosting of AI/ML technologies must comply. This includes contractors who develop AI models, deploy AI systems in DoD environments, or store and host AI services for defense operations.
Q2. When will the AI Framework requirements become mandatory for defense contractors? While CMMC 2.0 requirements began appearing in DoD contracts on November 10, 2025, the AI Framework lacks a specific implementation deadline. The Department of Defense must provide a status update to Congress by June 16, 2026, which will clarify when contractors must begin meeting the new AI-specific requirements. Contractors should monitor DoD announcements closely following this date to understand enforcement timelines.
Q3. What are the main security controls required for AI systems under CMMC? Defense contractors must implement four key categories of AI-specific security controls: data input validation and sanitization to prevent prompt injection attacks, model access controls with multi-factor authentication and role-based permissions, comprehensive AI output monitoring and logging integrated with SIEM systems, and adversarial attack prevention measures including continuous model validation and defensive testing.
Q4. How does Shadow AI affect CMMC compliance? Shadow AI refers to unauthorized AI tools that employees use without IT approval, such as browser extensions or personal AI accounts. This represents the most common compliance finding during CMMC assessments. Organizations with high shadow AI usage experience breach costs averaging $4.63 million, which is $670,000 more than those with low usage. Contractors must catalog all AI tools, implement technical controls to prevent unauthorized usage, and ensure CUI never flows to unapproved AI systems.
Q5. What immediate steps should defense contractors take to prepare for AI Framework compliance? Start by conducting a complete AI inventory of all tools in use, including browser extensions and personal accounts. Classify which tools interact with CUI and evaluate whether they meet NIST 800-171 controls. Remove non-compliant tools from CUI workflows, update your System Security Plan to document AI usage, and implement technical controls to prevent unauthorized AI access. Organizations should also conduct internal readiness assessments before scheduling official CMMC audits to identify and address compliance gaps early.