
The Pentagon wants commercial AI, and AI companies want the Pentagon’s business. But the terms that make or break these deals are often about data, security, and rights, not algorithms. That demand runs deep across U.S. defense customers and the defense industrial base, and AI companies are increasingly eager to supply it, directly or through primes, subcontractors, vendors, or technology partners. These transactions raise consequential legal issues that extend well beyond model performance, bias, or weapons-system integration. For privacy, security, product, IP, and government contracting teams, the early questions are practical but high-stakes: where the solution will run, what government or contractor data it will touch, whether Controlled Unclassified Information (CUI) will enter the company’s environment, whether prompts and outputs will be retained or reused, and what rights a government or defense customer may seek in AI-related deliverables.
The use case and contracting path both matter. AI sold into the defense market may support logistics, cyber, training, procurement, or other mission-support functions, or it may be integrated into, support, or influence weapons systems, where additional weapons-system rules may apply. Obligations may also depend on how the solution is acquired, including through the General Services Administration (GSA) Multiple Award Schedule, a governmentwide contracting program available to federal agencies. GSA has proposed a rule on basic safeguarding of AI systems and intellectual property rights that, if finalized and included in an applicable procurement, could add obligations related to government data, incident reporting, oversight, and downstream providers as well as potentially broad government ownership of modifications, customizations, configurations, and other broadly defined “Custom Development,” without corresponding rights for the AI provider. Companies should treat both use case and contracting path as threshold diligence questions.
Deployment is equally important. Companies should map where the application runs; where training, tuning, and inference occur; and where prompts, outputs, logs, and customer data are processed or stored. Requirements will differ depending on whether the solution is deployed in a defense agency or prime contractor environment, a customer-controlled environment, or provider-hosted SaaS. Even where the solution runs outside the provider’s environment, the company may still receive Controlled Unclassified Information (CUI) through proposals, implementation, support, or contract administration. Depending on the contract and flowed-down requirements, that may require a segregated environment aligned with NIST SP 800-171, Defense Federal Acquisition Regulation Supplement (DFARS) incident reporting obligations, and potentially Cybersecurity Maturity Model Certification (CMMC) requirements.
Customer data needs a sharp definition. Contracts should clearly address whether government, prime contractor, or subcontractor data may be used to train, fine-tune, or improve the provider’s model or service. For GenAI tools, customer data may appear in prompts, outputs, logs, or telemetry; even if the provider is not “training” on that data, it may still be processing or retaining information subject to enhanced obligations.
Not every “data rights” clause is about privacy or data processing. In defense contracting, data rights address government license and use rights in software, technical data, documentation, outputs, and other deliverables. These clauses can affect product strategy, IP protection, and valuation, so legal teams should identify them early and involve relevant stakeholders before accepting customer or flowed-down terms.
The practical takeaway: before negotiating defense-related contracts or supplier terms, companies should map the data flows, classify the data, confirm the contracting path and applicable flowdowns, and review privacy, cybersecurity, AI governance, government contracting, and data rights issues together.








