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Editor’s Note: President Trump’s recent executive order on artificial intelligence reshapes the relationship between federal and state regulators in a domain that increasingly touches litigation, compliance, and risk management. By pushing for preemption of state-level AI laws and tying federal funding to regulatory alignment, the administration is centralizing AI governance in ways that will ripple through procurement, recordkeeping, and courtroom defensibility. For cybersecurity, information governance, and eDiscovery professionals, this article examines what the shift means in practice—and what steps organizations can take now to prepare.

Industry – Artificial Intelligence Beat

Trump’s AI Executive Order Reshapes State-Federal Power in Tech Regulation

ComplexDiscovery Staff

State capitals are used to hard questions about federal power, but this time it is not over taxes or guns—it is about algorithms.

In the hours since President Donald Trump signed a sweeping executive order on artificial intelligence regulation, state lawmakers, civil liberties groups, and technology companies have been scrambling to understand what a single national rulebook for AI really means for how data is governed and disputes are litigated.

The order, signed and published on Dec. 11, seeks to create a uniform federal framework for AI and sharply limit the ability of states to enforce their own rules on how algorithms are built, deployed, and overseen. For cybersecurity, information governance, and eDiscovery professionals, that shift is not an abstract constitutional fight—it is a direct signal that the future of AI risk, recordkeeping, and evidence will be negotiated in Washington rather than in 50 separate legislatures.

A National Bid to Rein in States

At its core, the new executive order tells federal agencies to treat AI as an area where national interests trump local experimentation, with language aimed squarely at state-level efforts in places such as California and New York to impose tougher obligations on AI developers. The directive instructs the attorney general to stand up an “AI Litigation Task Force” within 30 days, charged with challenging state AI laws that are seen as incompatible with the administration’s preference for lighter-touch federal oversight.

The order does more than threaten lawsuits. It ties access to certain federal funds—most notably remaining dollars in the $42.5 billion Broadband Equity, Access and Deployment (BEAD) program—to whether states adopt AI policies that align with federal standards or, at a minimum, avoid what the administration casts as “cumbersome” or innovation-chilling rules. For organizations that operate data centers, telecom networks, and cloud services, that funding linkage turns AI policy positions into a new factor in infrastructure strategy and long-term risk assessments.

The Trump AI Playbook: Action Plan and Orders

The preemption order does not stand alone. It sits atop an AI policy architecture that the Trump administration has been constructing through the “America’s AI Action Plan” and a cluster of earlier executive orders on leadership, exports, and federal use of AI. An order signed in January—Removing Barriers to American Leadership in Artificial Intelligence—set the tone by directing agencies to eliminate regulatory obstacles and by commissioning the AI Action Plan to advance what the White House calls “global AI dominance.”

When the plan was unveiled in July, the administration paired it with three AI-related executive orders on accelerating federal permitting for data center infrastructure, promoting the export of the “American AI technology stack,” and preventing what the administration characterizes as “woke AI” in federal procurement. Together, these documents describe a deregulatory posture that prioritizes rapid AI deployment, streamlined permitting, and export promotion, while pressuring agencies to favor AI systems that meet ideological neutrality tests and to scrutinize state laws that might complicate that agenda.

What Preemption Looks Like in Practice

The new order translates that philosophy into concrete instructions. It directs the Federal Trade Commission to issue a policy statement explaining when state AI laws that require changes to “truthful outputs” of models are preempted by the FTC Act’s prohibition on deceptive practices. It also tells the special advisor for AI and cryptocurrency to develop legislative recommendations for a federal AI policy framework that would formally override conflicting state rules.

Other agencies are tasked with evaluating whether to adopt federal reporting and disclosure standards for AI models that would displace overlapping or inconsistent state requirements. The plan leaves some room for state action by carving out areas such as child safety protections, state procurement, and permitting reform for data centers, but does not define those exceptions with precision—notably including a catch-all for “other topics as shall be determined” by federal officials. That vagueness is already drawing criticism; the American Civil Liberties Union characterized the order as a “unilateral attack” on state regulation that uses funding and preemption as blunt tools to discourage aggressive privacy and accountability measures.

Federal AI Governance Meets Everyday Risk

For CISOs, DPOs, and discovery managers, the most immediate impact is the emerging assumption that AI risk management will be benchmarked against federal expectations, including revisions to NIST’s AI Risk Management Framework directed by the administration. The Office of Management and Budget has begun translating those expectations into concrete requirements for agencies, including mandates to avoid what it calls “biased” or “woke” AI and to document how AI systems are tested, monitored, and governed.

Even though those instructions technically apply to federal agencies, they will bleed into the private sector through procurement terms, certifications, and due diligence questions. One practical step for legal and compliance teams is to treat OMB directives and NIST updates as forward indicators for what regulators and courts may soon expect from any organization deploying high-impact AI—documented testing, clear accountability chains, and evidence that bias and security risks are being monitored over time.

Implications for Cybersecurity

From a security standpoint, the Trump AI framework pushes agencies to maximize “American AI” and to classify certain applications as “high impact,” triggering heightened due diligence. Agencies are required to conduct AI adoption maturity assessments and to discontinue noncompliant high-impact systems by April 2026, effectively forcing a rolling audit of AI used in mission-critical and sensitive contexts.

Security and risk leaders in the private sector can mirror that approach by building an internal high-impact register for AI use cases and by setting a clear date when noncompliant tools will be decommissioned or isolated. That type of policy, tied to real deadlines and supported by cross-functional signoff, makes it easier to demonstrate diligence to regulators or in post-incident discovery, especially if a breach or AI failure raises questions about how a model was vetted, monitored, and patched.

Data, Records, and Discovery

For information governance and eDiscovery professionals, the most pressing question is what a federally led AI framework will mean for records management, explainability, and the admissibility of AI-generated insights. The administration’s emphasis on export promotion and minimal burdens on AI businesses suggests that mandatory algorithmic transparency or auditability requirements are unlikely to originate from the White House in the near term.

That makes internal governance all the more important. In practice, teams can respond by insisting on three basics whenever AI is introduced into workflows that may later be litigated: retention of model and configuration metadata, logs that allow reconstruction of key decisions, and clear documentation of training data sources and risk controls. Those records will be vital if courts eventually have to weigh whether an AI-assisted review, classification, or investigation process is trustworthy, particularly in an environment where the federal government is more focused on innovation speed than on mandating explainability.

A Contested Future for AI Rulemaking

The executive order arrives after Congress twice rejected similar preemption measures—first stripping a ten-year moratorium on state AI regulation from the reconciliation bill in July, then blocking an attempt to insert preemption language into the National Defense Authorization Act in early December. By acting unilaterally, the administration has bypassed legislative debate, a move that is expected to draw legal challenges from states and advocacy groups that see it as an overreach of executive power and an intrusion on traditional state authority over consumer protection and civil rights.

Legal analysts note that while the executive order outlines an aggressive federal stance, the extent to which it can preempt state law without congressional backing remains uncertain. Courts will need to assess the constitutional limits of executive power, particularly given prior legislative resistance to such preemption efforts.

Courts will have to decide how far the administration can go in using preemption theories and funding conditions to discourage state AI experimentation, and whether Congress needs to act before states are sidelined.

For now, the message to enterprises is straightforward: watch Washington, but do not ignore Sacramento, Albany, or Brussels. Even if parts of the order are eventually narrowed, the current White House strategy signals that large AI providers and their customers should plan for discovery, breach response, and governance in a world where federal guidance is the primary reference point and aggressive states are treated as outliers to be challenged rather than partners to be emulated.

For professionals who live at the intersection of security, governance, and legal process, the executive order is best viewed as a warning shot and an opportunity—an early look at how AI governance battles will shape the evidence they collect, the systems they defend, and the standards they must meet. If national AI policy is now being written with litigation and preemption in mind, how will your organization adjust its AI governance to be ready for both regulators and the record?

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Photo of Alan N. Sutin Alan N. Sutin

Alan N. Sutin is Chair of the firm’s Technology, Media & Telecommunications Practice and Senior Chair of the Global Intellectual Property & Technology Practice. An experienced business lawyer with a principal focus on commercial transactions with intellectual property and technology issues and privacy

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Alan N. Sutin is Chair of the firm’s Technology, Media & Telecommunications Practice and Senior Chair of the Global Intellectual Property & Technology Practice. An experienced business lawyer with a principal focus on commercial transactions with intellectual property and technology issues and privacy and cybersecurity matters, he advises clients in connection with transactions involving the development, acquisition, disposition and commercial exploitation of intellectual property with an emphasis on technology-related products and services, and counsels companies on a wide range of issues relating to privacy and cybersecurity. Alan holds the CIPP/US certification from the International Association of Privacy Professionals.

Alan also represents a wide variety of companies in connection with IT and business process outsourcing arrangements, strategic alliance agreements, commercial joint ventures and licensing matters. He has particular experience in Internet and electronic commerce issues and has been involved in many of the major policy issues surrounding the commercial development of the Internet. Alan has advised foreign governments and multinational corporations in connection with these issues and is a frequent speaker at major industry conferences and events around the world.

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