AI Revolutionizes Compliance: A New Era with CMMC

The defense industrial base has adopted new practices, such as engineers asking a copilot to refactor code, program teams using a chatbot to draft responses, and analysts using retrieval systems for summarizing technical notes. While these practices may seem fast and modern, they pose a risk when it comes to handling sensitive data, especially with the implementation of the Cybersecurity Maturity Model Certification (CMMC) in 2026.

With the introduction of AI into contractor environments, the compliance conversation has shifted to include new considerations related to AI usage. Contractors must now be mindful of potential unauthorized disclosure, retention, and reuse of sensitive data when using AI for tasks such as pasting technical text into a copilot or uploading documents into a retrieval assistant. Additionally, using AI-generated code in contract performance raises concerns about provenance, secure review, vulnerability introduction, and output trustworthiness.

Leaders in the defense industry must shift their focus from simply asking whether they are allowed to use AI to determining which AI use cases can be governed at the level required for handling sensitive information. This shift in perspective is necessary to ensure compliance with regulations and to prevent data mishandling through AI tools. It is important for contractors to understand the implications of AI usage in terms of data security and to implement proper governance measures to mitigate risks associated with AI integration. How should contractors govern prompts, retrieval stores, and embeddings in the context of AI era storage challenges?

In the AI era, contractors need to treat prompts, retrieval stores, embeddings, and other AI artifacts as governed information handling surfaces rather than transient conveniences. If these systems can access Controlled Unclassified Information (CUI), then they must be subject to strict rules around ingestion, indexing, access controls, retention, logging, and incident response. This approach ensures that data security is maintained across all systems touching controlled information.

What should contractors consider when dealing with AI-generated code and AI-assisted engineering?

Contractors working with AI-generated code must understand that such code is not inherently compliant and requires thorough review, provenance tracking, validation against security requirements, and human approval before being integrated into production systems. CISA’s emphasis on secure deployment and asset understanding underscores the importance of governing AI-generated artifacts within secure development processes. Security should be integrated into product requirements rather than added as an afterthought.

Do current contractor practices align with the goals of CMMC?

In many cases, current contractor practices do not fully align with the goals of the Cybersecurity Maturity Model Certification (CMMC). The Government Accountability Office (GAO) has highlighted implementation challenges in the revised CMMC program, particularly regarding support for small companies, workforce readiness, and comprehensive implementation strategies. Compliance success may vary across the defense ecosystem, with subcontractors potentially posing risks to mission-critical data through unmanaged AI use. Contractors need to prioritize AI governance across all aspects of their operations to meet CMMC requirements.

How can contractors establish a defensible AI governance posture?

To establish a defensible AI governance posture, contractors should implement data classification rules for AI use, maintain approved tool boundaries, govern prompts and retrieval processes, require human review of generated code, extend AI governance expectations to suppliers, and ensure continuous monitoring and incident readiness for AI-enabled systems. By aligning AI governance with existing security practices and contractual obligations, contractors can mitigate risks associated with AI use in defense contracting. Leadership should prioritize formal oversight of AI governance, define defense-specific AI use cases, and treat supplier AI use as part of third-party risk management and contract performance assurance. By adopting a structured and comprehensive approach to AI governance, contractors can enhance data security and compliance with federal regulations. Incorporating AI into defense operations has raised significant compliance challenges, particularly in areas such as enterprise risk management, secure engineering, data governance, acquisition review, and compliance oversight. The focus is no longer solely on whether AI usage is permitted but rather on whether contractors can demonstrate control over AI tools and data handling processes.

Contractors must be able to provide evidence of approved AI tools, permitted data types, authorized user access, boundaries for prompts and uploads, retention management, output review processes, code validation, governance of supplier utilization, and procedures for detecting and reporting incidents involving AI-assisted workflows. Failure to do so indicates not just a gap in AI policy but a broader maturity gap in compliance readiness.

The arrival of the Cybersecurity Maturity Model Certification (CMMC) underscores the importance of demonstrating the real, repeatable, and assessable nature of cybersecurity practices. Meanwhile, the rapid advancement of AI technology has made it easier to overlook potential vulnerabilities in data handling processes. This shift in compliance focus requires a deeper understanding of the intricate paths data takes within AI systems and the need to safeguard against potential threats.

Successfully navigating these compliance challenges requires a proactive approach to secure data handling practices in the age of AI. It is no longer sufficient to focus solely on traditional cybersecurity measures for servers and devices. Instead, contractors must also prioritize securing prompts, context windows, vector stores, generated artifacts, and data pathways within AI systems. The responsibility to protect data and demonstrate control remains paramount, necessitating a comprehensive approach to compliance readiness.

By leveraging guidance from authoritative sources such as the Cybersecurity and Infrastructure Security Agency, National Institute of Standards and Technology, Government Accountability Office, and Department of Defense, contractors can enhance their understanding of secure AI integration and data governance practices. Embracing a proactive mindset towards compliance and cybersecurity maturity will be essential for contractors seeking to thrive in the evolving landscape of AI-driven defense operations.

Joe Guerra, a seasoned technology and cybersecurity professional, brings a wealth of experience in software engineering, AI, and technical leadership to his work at FEDITC, LLC. Through FEDITC’s commitment to delivering secure, mission-aligned solutions for government and defense missions, Joe and his team support critical operations worldwide by implementing robust cybersecurity measures, cloud services, engineering solutions, and continuous improvement practices. By prioritizing secure operational execution and compliance with industry standards, FEDITC helps organizations deploy resilient and effective technology solutions in alignment with evolving regulatory requirements.

For more information on FEDITC’s services and capabilities, visit https://feditc.com/ or contact Joe Guerra at [email protected]