Closing the National AI Framework Enforcement Gap Support the White House National AI Framework by specifying the enforcement infrastructure required to make its policy protections verifiable at the point of AI inference. Submitter's Note: This StratML rendition was compiled from the source by ChatGPT and lightly edited in the form at https://stratml.us/forms/Claude/Part1.html Basil C. Puglisi, MPA BCP 9454e535-6c61-4239-b70d-1707482539ae Human-AI Collaboration Strategist | basilpuglisi.com ^^ Building Influence with Integrity ~ Basil C. Puglisi is a strategic consultant, digital media expert, educator, and author with a legacy rooted in teaching others how to influence with integrity. Since 2009, he has been at the forefront of digital communications—long before “influencer” became an industry term. His work spans consulting, publishing, and public speaking, all grounded in one core belief: clarity creates confidence, and confidence builds influence. American AI governance is both competitive and verifiably enforceable at the point of execution. 328d2103-22a3-4069-b52e-d1109c0e6089 To advance a national AI policy framework whose protections for children, creators, communities, markets, and national security are backed by technical infrastructure that enables auditability, verification, and practical enforcement. f338ea0a-a231-45db-babc-14902e0c137b Accountability Make AI policy commitments verifiable in practice rather than merely declarative in law. Transparency Provide evidence of what AI systems do with regulated data and generated outputs at the point of inference. Plurality Reduce structural dependence on single-provider AI deployments and preserve multi-provider governance options. Security Protect sensitive data and national interests through tamper-evident and customer-verifiable controls. Competitiveness Strengthen American AI leadership by building infrastructure that is operationally resilient and trustworthy. Enforcement Infrastructure Recognize and close the gap between national AI policy aspirations and the technical means required to verify and enforce them at the point of inference. 66d81800-c1ee-4b50-951b-28fe11cf1cd5 1 Recognition Acknowledge that the seven pillars of the White House framework depend upon enforcement infrastructure that is not yet specified in current federal law or in the recommendations themselves. e774abfc-94d5-4c66-a0b9-3e873a02c2dc 1.1 Verification Establish customer-verifiable evidence mechanisms showing how AI platforms process regulated data and generate outputs that affect real decisions. 92046a55-620d-4141-888b-8d3e58b119d0 1.2 Auditability Require technical capabilities that make AI system behavior inspectable, tamper-evident, and auditable at the moment of processing. 66e41788-73b2-40ce-952a-3b1e20e457ec 1.3 Applicability Apply enforceable safeguards to sensitive data contexts including healthcare, education, finance, and government information processed through commercial AI services. 25df334d-f7d4-4f53-a2b1-be453e9e68fb 1.4 Bias Detection Address the structural risks posed by single-provider AI deployments, especially undetected cultural bias and automation bias in high-volume decision environments. 8e866bb6-f347-479a-ae41-766eac8b4b8d 2 Plurality Prevent single-provider lock-in so that federal workflows retain access to cross-provider comparison and governance diversity. 8639f88e-bc0f-4eb2-a8b5-a6d962b2271f 2.1 Divergence Surface differences among AI system outputs so that hidden cultural defaults and shared blind spots can be identified before shaping policy, intelligence, or military decisions. a8c6bd79-01f9-4e20-b5eb-a4d6aced76bf 2.2 Oversight Strengthen human oversight by reducing unexamined deference to machine recommendations and by presenting comparable outputs from multiple providers. dde52606-514c-4506-a688-0da39458586d 2.3 Resilience Reduce strategic vulnerability arising from WEIRD-correlated defaults and other systemic biases that adversaries with different institutional priors may exploit. 29999da8-20ea-45a9-9e74-d1d0eac4416f 2.4 Provider Plurality Promote market and governance conditions under which no single AI platform can dominate the federal government’s access to AI capabilities and oversight options. 36ea0b3c-c245-498a-bd3c-8b9b8abb3769 3 Accessibility Require API accessibility for AI companies operating in the United States so that agencies and governance tools can interact with multiple providers. 2327e32f-5419-4fb1-b5be-1e85ef0827e6 3.1 Competition Extend anti-concentration protections and related antitrust principles to AI infrastructure. 9e1ede65-25ca-4d28-9200-1c5abdc369ce 3.2 Investment Support small AI platforms through existing federal mechanisms such as SBIR and STTR in order to broaden the provider base. 11c479f0-b9bd-4754-9ca8-5a2016ce688e 3.3 Governance Orchestration Evaluate non-cognitive governance infrastructure capable of enforcing policy process requirements without introducing a new regulator. 23fdbcda-49eb-4ab5-938b-bdcec5617b7c 4 Dispatch Send identical prompts or requests to multiple AI platforms in a uniform and policy-governed manner. 767be4e1-5c37-422e-afa0-32720fdfffcd 4.1 Collection Collect and preserve all provider responses without semantic modification. 8f69d9d7-dfb9-4753-a923-33a4f22f5818 4.2 Routing Deliver provider outputs to human decision makers in a manner that preserves comparability and oversight. ac6c5af7-34b1-42ac-bc11-cefc7ebb801b 4.3 Logging Record deterministic operations such as dispatch, collection, routing, pausing, hashing, and reporting to create a cryptographic audit trail. 0560c854-39c2-45de-b86d-db8f33fcdf4c 4.4 Evaluation Conduct zero-new-appropriation pilot activity sufficient to generate baseline evidence on the feasibility and utility of such governance infrastructure. cc5fd390-33c1-4dd4-8f75-e6156d5bee7f 4.5 Confidential Inference Establish a narrow federal standard for customer-verifiable protection of regulated sensitive data during AI inference. 7dafca91-5ff2-42cf-99eb-2271ebc1c748 5 Standard Direct the development of a Verified Confidential Inference Standard focused specifically on data protection during processing. e45c0492-06a0-4fc9-8808-d76c4a297b0a 5.1 Classification Define profiles or categories suitable for determining applicable protections when AI platforms process sensitive external data. c93b333e-de7a-46a3-8630-093373df581b 5.2 Harbor Provide safe-harbor treatment for entities complying with the applicable verified inference standard. 4fdd11ff-9cd5-4964-93b2-7e1637aff77b 5.3 Federalism Establish a federal floor for confidential inference while preserving the role of existing agencies rather than creating a new federal gatekeeper. 18a979d8-e67c-4e2c-a9a9-a1494fabd192 5.4 Federal Evaluation Direct federal institutions to assess whether proposed enforcement infrastructure is feasible, desirable, and improvable. af425425-892a-4b8a-9bb7-49ce1fdf56bf 6 Assessment Direct NIST and GSA to evaluate published infrastructure concepts as starting points for federal development. 923ca3bf-8e55-4fd8-bb12-88505026bc1f 6.1 Improvement Encourage federal agencies to improve upon existing published specifications where better designs are possible. 7af36d01-a771-46ed-b44c-cdc88611d1fb 6.2 Legislation Demand legislative proposals that pair policy commitments with enforceable technical architecture rather than policy language alone. a3493d51-0bdf-49f6-bb34-2a05cf49b5a8 6.3 2026-03-21 2026-04-05 https://basilpuglisi.com/open-letter-white-house-national-ai-framework-enforcement-gap/ Owen Ambur Owen.Ambur@verizon.net