Cybersecurity Maturity Beyond CMMC: What the Next Level of Defense AI Governance Looks Like

Cybersecurity Maturity Beyond CMMC: What the Next Level of Defense AI Governance Looks Like

CMMC 2.0 is finally moving from rulemaking purgatory into actual enforcement. The final rule hit the Federal Register on October 15, 2024, with phased implementation starting in early 2025. For most defense contractors, the immediate priority is getting through Level 2 assessments without losing contract eligibility. Fair enough. But if you're spending all your energy on CMMC compliance and not watching what's forming behind it, you're going to be caught flat-footed by the next wave.

Because CMMC was designed to protect Controlled Unclassified Information. It was never scoped to govern how AI systems operate within defense environments. And the DoD is now moving fast on exactly that question.

The Governance Gap CMMC Was Never Meant to Fill

CMMC maps to NIST SP 800-171, which is fundamentally about protecting data at rest, in transit, and in use. Access controls, encryption, incident response, audit logging. These are necessary but insufficient when your environment includes machine learning models making or supporting operational decisions.

Think about what CMMC Level 2's 110 controls actually cover. They address who can access a system, how data flows between systems, and whether you're logging anomalies. They do not address whether an AI model's training data was poisoned, whether an algorithm's outputs are explainable to a human decision-maker, or whether a model has drifted from its validated performance envelope. These are fundamentally different risk categories.

The DoD knows this. The 2023 Data, Analytics, and AI Adoption Strategy (published by the Chief Digital and AI Office, or CDAO) explicitly calls for "responsible AI" principles to be embedded across the acquisition lifecycle. DoD Directive 3000.09, updated in January 2023, tightened requirements around autonomous and semi-autonomous weapons systems, including mandates for human judgment in the use of force. And Executive Order 14110, signed October 30, 2023, directed agencies to establish AI governance frameworks with specific reporting deadlines through 2025.

These aren't aspirational documents. They're creating concrete obligations that will flow down to contractors, just like DFARS 252.204-7012 flowed CUI protection requirements down before CMMC formalized them.

What "Post-CMMC" AI Governance Will Likely Require

If you look at the trajectory, several requirements are converging into what will probably become a structured framework within the next two to three years. Here's what's taking shape:

Model Provenance and Supply Chain Integrity

CMMC cares about your software bill of materials in a general sense. Future AI governance will demand something more specific: full provenance documentation for training data, model architectures, and fine-tuning datasets. The NIST AI Risk Management Framework (AI RMF 1.0, released January 2023) already outlines this under its "Map" and "Govern" functions. Expect DoD contracts to start requiring AI BOMs (bills of materials) that trace a model's lineage the way you'd trace a physical component in a weapons system.

Continuous Model Monitoring

Static certification won't cut it for AI systems. A model that passes validation in March can drift by June if its input distribution shifts. The CDAO's Responsible AI Toolkit, released in mid-2023, includes testing and evaluation frameworks that assume ongoing monitoring. This is analogous to how CMMC Level 2 requires continuous monitoring of your security posture, but applied to model performance, bias metrics, and output reliability.

Explainability Requirements Tied to Decision Criticality

Not every AI system needs the same level of explainability. A predictive maintenance model for vehicle fleets has different stakes than a targeting recommendation system. The DoD's AI governance approach is likely to tier explainability requirements based on the criticality of the decision being supported. DoD Directive 3000.09 already distinguishes between levels of autonomy; expect that logic to extend into broader AI governance.

Adversarial Robustness Testing

MITRE's ATLAS framework (Adversarial Threat Landscape for AI Systems) catalogs known attack vectors against ML systems, from data poisoning to model evasion. The DoD has funded significant research through DARPA's Guaranteeing AI Robustness Against Deception (GARD) program. It's reasonable to expect that future defense AI governance will require contractors to demonstrate adversarial robustness testing as a condition of deployment, similar to how penetration testing is expected under CMMC.

The Flowdown Problem Is Already Here

One thing that made CMMC implementation painful was the surprise many subcontractors experienced when prime contractors started flowing down requirements. The same dynamic is already starting with AI governance.

In 2024, several large primes began including AI-specific clauses in subcontract agreements, requiring disclosure of AI tools used in deliverable production, restrictions on generative AI for handling CUI, and documentation of any automated decision-making in quality assurance processes. These aren't standardized yet, which actually makes them harder to manage. Every prime is writing their own version.

The Defense Federal Acquisition Regulation Supplement (DFARS) will eventually standardize these requirements, but we're in the messy interim period where contractual obligations are running ahead of formal regulation. If you've been through the DFARS 252.204-7012 era before CMMC, this feels familiar.

Practical Steps for Companies Already in the Defense Supply Chain

You don't need to wait for a formal "CMMC for AI" framework to start preparing. Several moves make sense right now:

• Inventory your AI touchpoints. Know where machine learning models, generative AI tools, and automated decision systems touch your defense work. Include third-party tools your employees might be using informally.
• Map to NIST AI RMF. Even though it's voluntary today, the AI Risk Management Framework is the most likely foundation for future DoD AI requirements. Aligning now reduces future remediation costs.
• Document model provenance. For any AI system involved in defense deliverables, start building documentation of training data sources, model versions, validation results, and deployment conditions.
• Establish acceptable use policies for generative AI. The DoD's September 2023 generative AI guidance made clear that uncontrolled use of tools like ChatGPT in CUI environments is a problem. Your policies should be specific about what's allowed, on which systems, and with what oversight.
• Build adversarial testing into your validation process. Even basic red-teaming of AI outputs against known ATLAS attack categories puts you ahead of most of the supply chain.

The Integration Challenge

The real difficulty isn't any single requirement. It's managing AI governance alongside CMMC, ITAR, DFARS, and whatever else applies to your specific contracts. These frameworks overlap in some areas and leave gaps in others. Your access control policies under CMMC affect who can interact with AI systems. Your ITAR obligations affect whether model training data can be processed offshore. Your incident response plan needs to cover AI-specific failure modes alongside traditional cyber incidents.

Organizations that treat each framework as a separate compliance silo will spend more, respond slower, and miss the intersections where real risk lives.

How FirmAdapt Addresses This

FirmAdapt's platform is built to manage overlapping regulatory frameworks as a unified compliance posture rather than parallel workstreams. For defense contractors navigating CMMC while preparing for emerging AI governance requirements, this means mapping controls across NIST SP 800-171, NIST AI RMF, DFARS clauses, and prime-specific flowdown requirements in a single environment. The platform tracks which controls satisfy multiple frameworks simultaneously, so you're not duplicating effort or maintaining separate documentation systems.

On the AI governance side specifically, FirmAdapt supports model inventory management, provenance documentation, and policy enforcement for generative AI use in regulated environments. As DoD AI requirements formalize, the platform is designed to incorporate new framework mappings without requiring a rebuild of your existing compliance architecture.