The White House is moving closer to a voluntary framework under which AI companies would submit their most advanced models to the federal government before public release. The White House’s Office of the National Cyber Director reportedly circulated the draft framework by to OpenAI, Anthropic, and Google, and those companies jointly submitted edits. Although the review process details are not yet public, the continued federal interest in pre-release review of frontier AI models follows earlier discussion of a possible FINRA-like watchdog for advanced AI systems. 

For businesses, the key takeaway is that voluntary AI governance is increasingly becoming a practical expectation, even where formal legal mandates remain unsettled. Companies developing, deploying, or procuring AI tools should be prepared to document model governance, risk assessment, testing, security controls, data provenance, privacy considerations, and human oversight in a way that can stand up to regulator, customer, investor, and board scrutiny. Even if the initial federal framework applies most directly to major AI model developers, downstream users should expect those norms to flow through vendor diligence, contract terms, audit rights, procurement questionnaires, and enterprise AI policies.

Business clients should use this moment to get their AI governance house in order: inventory AI systems and vendors, classify higher-risk use cases, update privacy and security reviews for AI-enabled tools, and build clear internal approval processes before rolling out new tools . For companies buying AI products, contracts should address testing, transparency, cybersecurity, data use restrictions, confidentiality, model training rights, regulatory cooperation, incident notice, and responsibility allocation if the tool produces harmful or noncompliant outputs. The practical step now is not to wait for a final federal rule, but to establish a defensible governance record that reflects a thoughtful assessment of risks, sound decision-making processes, and clear oversight and explanations of AI system management.