Regulatory and Government

In April 2026, California’s Department of Motor Vehicles released final rules governing how self-driving vehicles are tested and deployed in the state, capping more than a year of drafting and public comment. The rules separate testing from broader commercial deployment and set up three staged permits: testing with a safety driver, testing with no driver, and full deployment. Most manufacturers must log at least 50,000 autonomous miles before each step up, with a lower 10,000-mile threshold for low-speed vehicles, and a much higher 500,000 miles for heavy trucks, which became eligible in California for the first time. Applicants must also submit a detailed safety case explaining why the vehicle is safe, which the DMV may review with outside experts.

Other changes include a new traffic-violation notice police can issue, triggering 24- or 72-hour reporting deadlines; required first-responder interaction plans and prompt responses to emergency geofencing requests; and training and licensing standards for remote drivers and remote assistants. Large passenger buses remain ineligible, apart from a narrow exception.

Children’s privacy regulations globally are shifting away from notice-and-consent models toward outright access restrictions, age-appropriate design mandates, categorical advertising prohibitions, and app-store-level age verification, as illustrated by Australia’s December 2025 enforcement of its under-16 social media ban. In the United States, similar legislative efforts have faced significant First Amendment challenges that have stalled most state laws restricting minor access to social media and AI platforms, while age-appropriate design requirements inspired by California’s law have been enacted in several states. Several states, including Oregon, have enacted categorical bans on targeted advertising to known minors, while app-store-level age verification laws imposing age-signal obligations on app developers are also subject to active litigation. Companies operating consumer-facing digital services should map where minors interact with their products, review advertising practices involving teens, and prepare for the eventual downstream effects of age-signal regimes.

On June 2, 2026, President Trump signed an executive order called “Promoting Advanced Artificial Intelligence Innovation and Security” building on the cyber strategy his administration released in March 2026, which sets deadlines for federal agencies in three areas: using artificial intelligence (AI) to defend government and critical infrastructure systems, evaluating the most capable AI models, and prosecuting people who use AI to break into computers or commit fraud. It directs the Cybersecurity and Infrastructure Security Agency (CISA) to issue binding directives within 30 days, and the United States Department of Treasury to create an industry clearinghouse that coordinates vulnerability scanning and patching.

Within 60 days, agencies must build a classified benchmarking process to decide when a model counts as a covered frontier model, plus a voluntary program letting developers give the government early access before a wider release. For companies, the signal is that AI security and cybersecurity are being treated as one governance topic, and AI-enabled defenses may become a baseline expectation in government and critical infrastructure contracts.

The U.S. Food and Drug Administration is deepening its own use of artificial intelligence. Less than a year after launching its internal AI tool, Elsa, the agency released Elsa 4.0 and connected it to a new unified data platform that pulls together roughly 40 previously separate systems, so staff can query agency data directly instead of uploading documents each time. New features include custom AI agents, document generation, data analysis and visualization, and better search. FDA says humans stay involved at every step and that the model is not trained on data submitted by regulated companies.

On the same day, the then-Commissioner announced a pilot of “one-day inspections” for facilities that AI-driven risk analysis flags as lower risk. These are short screening visits meant to supplement, not replace, full inspections. More than 40 have been completed since April 2026, mostly ending with no action indicated, though inspectors can still issue formal observations and can expand the visit on the spot. Companies are not told in advance whether they are in the pilot, so life sciences startups with manufacturing or clinical sites should stay ready for either format. Complex or higher-risk operations, such as sterile manufacturing, remain on the standard inspection track.

Two February 2026 federal court decisions addressed how existing privilege and work product doctrines apply to generative AI, with one court (in United States v. Heppner) denying privilege and work product protection where an executive used an AI platform without adequate confidentiality safeguards and without counsel’s direction, and another (in Warner v. Gilbarco) holding that a pro se litigant’s AI-assisted analysis via a public AI platform was protected work product. These decisions signal that courts may apply existing doctrines to new technology in the legal context. Key considerations include whether the AI platform’s terms and privacy policies preserve confidentiality, the extent of attorney involvement in the AI-assisted work, and whether the materials were prepared in anticipation of litigation. Organizations should inventory AI tools and review their confidentiality terms, establish guardrails against AI use for legal analysis, and consider updating litigation hold protocols to account for AI-generated materials such as prompts and outputs.

The U.S. Copyright Office’s Part III report on AI and copyright signals that companies cannot assume generative AI training is automatically allowed under “fair use.” The report says copyright issues may arise at several stages: when developers collect and copy protected material for training the model, when a model memorizes protected material in its internal “weights,” and when AI outputs closely resemble copyrighted material. It emphasizes that fair use must be judged case by case, looking at why the original work was used, whether the use is commercial or transformative, how much of the original work was copied, whether guardrails prevent copying in outputs, and whether the AI system harms existing or potential markets for the original work. The report also points to licensing and possible policy reforms as ways to address uncertainty, but overall it takes a cautious approach that is more favorable to copyright owners than to unrestricted generative AI training.

As AI vendors become integral to business operations, their potential bankruptcy creates complex challenges under a Bankruptcy Code that has yet to fully adapt to AI technology. The first of a two-part series, this article focuses on intellectual property considerations: specifically, how companies that license AI tools or models can protect their continued access when a vendor enters bankruptcy. Among the key risks is a debtor’s ability to reject executory contracts, which could strip away the ongoing services that make a licensed AI tool usable, even where the bare license itself survives. Proactive contractual measures, including escrow arrangements and source code and deployment rights, can mitigate these risks before a vendor crisis occurs.

The second of a two-part series, this article examines consumer privacy risks and strategic implications that arise when an AI vendor files for bankruptcy. Key issues include how trained models, fine-tuned weights, and training data derived from sensitive customer-provided data may be treated as bankruptcy estate assets that can be sold to third parties, how those model artifacts can enable reconstruction or extraction of personally identifiable data even without a sale of the raw data, and whether courts will classify AI vendors as “critical vendors” or “utilities” — designations that carry significant consequences for service continuity and restructuring negotiations. Companies should address these risks proactively through contract terms and vendor risk management protocols before a crisis occurs.

The Court of Justice of the European Union has clarified whether a health-related product counts as a “medical device” in the EU, which determines whether the heavy compliance obligations of the Medical Devices Regulation apply. In Case C-427/24, Diagramm Halbach, the Court held that the analysis has two parts: what the manufacturer says the product is for, drawn from labeling, instructions, and marketing, and whether the product objectively performs one of the specific medical functions the regulation lists, such as diagnosis, monitoring, or treatment of disease. Marketing claims should not be considered in isolation and may not pull a product into the regime if it does not actually perform a medical function, and equally a company cannot avoid the rules by staying quiet about an obvious medical use. For founders building software or AI-enabled health tools, the ruling sharpens the line between administrative or organizational features and functions that genuinely inform clinical decisions, and that distinction determines whether CE marking and conformity assessment are required before selling in the EU.

AI is reshaping drug discovery and development by accelerating research timelines, optimizing clinical strategies, and improving manufacturing efficiency, but adoption brings a complex web of legal considerations that life sciences companies must navigate. This article, published in Drug Discovery World, examines regulatory pathways for AI-assisted drug development, intellectual property ownership challenges, and data protection and privacy obligations arising from AI model training, and outlines practical steps organizations can take to capture the benefits of AI while managing regulatory, IP, and contractual risks.

On July 24, 2025, the California Privacy Protection Agency approved sweeping new regulations covering cybersecurity audits, risk assessments, and automated decisionmaking technologies (ADMT), pending review by California’s Office of Administrative Law. Certain businesses will be required to undergo annual independent cybersecurity audits beginning in 2027, phased in by company revenue, covering 18 components of a cybersecurity program and culminating in executive certification. Businesses must also conduct risk assessments before engaging in activities such as selling personal information, processing sensitive data, or using ADMT for significant decisions, with California uniquely requiring evaluation of “negative impacts” to consumers. Beginning April 1, 2027, businesses using ADMT for significant decisions must provide pre-use notices, access to information about how the ADMT works, and opt-out rights, though the regulations clarify that ADMT excludes technologies where a human retains authority to review and override the output. Additional provisions strengthen opt-out notification requirements and prohibit dark patterns in consent mechanisms, with several of these provisions already in effect as of January 1, 2026.

On May 21, 2026, the New York State Department of Financial Services (“DFS”) issued two coordinated Industry Letters: a letter on Heightened Cybersecurity Risks Associated with Frontier AI models (the “AI Advisory”) and accompanying Guidance on Measures Regulated Entities Should Consider in a Heightened Cybersecurity Threat Environment (the “Guidance,” and together, the “May 2026 Publications”). The AI Advisory builds on DFS’s October 2024 guidance on cybersecurity risks arising from artificial intelligence (AI), but is narrower in focus. Specifically, it addresses frontier models that may materially increase the speed and effectiveness of vulnerability discovery and exploitation.

The May 2026 Publications are not new rulemaking — both Industry Letters explicitly state so — but they are meaningful supervisory guidance: DFS identifies frontier AI Models as a technological development that may materially change the threat environment and instructs covered entities to evaluate whether their existing Part 500 programs remain adequate in light of that changed risk. The publications merit attention from DFS-regulated entities because they identify a specific class of emerging technology that DFS views as material to cybersecurity risk management under Part 500. That attention is warranted not only because the May 2026 Publications identify risks DFS views as material under Part 500, but also because DFS has cited prior Industry Letters in Part 500 consent orders, underscoring that such guidance can have practical supervisory and enforcement significance.

On August 19, the U.S. Federal Trade Commission (FTC or Commission) proposed an “Enforcement Policy Statement Regarding Personalized Pricing.” The statement explains that the FTC will prioritize enforcing its consumer protection mandate to prevent companies from varying prices based on individual consumers’ personal data without certain disclosures. The public — businesses and consumers alike — will have an opportunity to provide feedback on the policy statement. Comments were due by September 25.

As used by the FTC, “personalized pricing” means the practice of setting the price offered to a particular consumer based on that consumer’s personal data or inferences drawn from it. Importantly, the FTC does not treat every individualized or variable price as personalized pricing. It distinguishes dynamic pricing based on market conditions, such as changes in supply and demand that affect everyone in the same market, including highly localized rideshare demand; regional price differences attributable to taxes, regulation, or local market conditions; and prices that inherently depend on a purchaser’s individual characteristics — its examples are insurance and credit, where individualized risk legitimately affects price.

Federal health regulators settled four ransomware investigations on April 23, 2026, with a health provider network, an imaging provider, a third-party benefits administrator, and a self-funded health plan. Together the breaches exposed health and financial data for more than 427,000 people, and the companies agreed to pay a combined US$1,165,000 and to follow corrective plans monitored for two years. The common thread was not how the companies responded after the attacks but what they had failed to do beforehand: none had a complete, current assessment of where sensitive health data lived, how it moved through their systems, and what could go wrong.

For a startup handling health data, or acting as a vendor to one, the practical signal is that regulators are judging security preparation, not just incident response. Expectations include an up-to-date inventory of systems and data flows, encryption, multi-factor login, logging, vulnerability scanning, and training. Pending amendments to the HIPAA Security Rule would make many of these steps mandatory, with roughly 240 days to comply once finalized.

Tokenization has moved past the theoretical stage. Exchanges have been cleared to trade certain securities in tokenized form, blockchain-based transfer agents are registering with regulators, and the SEC is revisiting foundational rules for ownership records maintained on distributed ledgers. For companies, the question is no longer whether a tokenized security is a security. It is how the rules governing ownership records, transfer agents, custody, settlement, and secondary trading apply when the security lives on blockchain infrastructure.

For several years, the debate centered on whether a given token was a security. SEC staff has now answered the question that matters to most operating companies: a security formatted as, or represented by, a crypto asset remains a security, and the format in which ownership is recorded does not change the analysis. The applicable registration, disclosure, and intermediary requirements do not disappear merely because the security is represented on a blockchain. The practical work has therefore moved downstream — to how the instrument is issued, recorded, held, transferred, traded, and settled.

The SEC Begins to Build an Architecture for Tokenized Securities

The SEC’s January 2026 Staff Statement on Tokenized Securities organizes tokenization into four broad models. Issuer-sponsored structures may be integrated, where the token is the security itself and the issuer’s on-chain record is part of its securities infrastructure, or non-integrated, where the token is linked to a security that remains recorded off-chain. Third-party structures may be custodial, where the third party holds the underlying securities that back the token, or synthetic, where it does not hold the underlying and the token represents a separate claim or exposure. These are not merely technical variations. Depending on the model, a holder may own the security itself or a claim against an intermediary. That distinction can mean different rights in an insolvency, different disclosure considerations, and, in some structures, the possibility that the instrument is a security-based swap. The practical takeaway is straightforward: architecture matters. Companies should identify who maintains the authoritative ownership record, what the token legally represents, which party owes the holder what, and what happens when it moves.

Transfer Agency and the Ownership Record Take Center Stage

The SEC has proposed the first substantial overhaul of its transfer agent rules since the late 1970s and early 1980s, aimed squarely at this problem. SEC staff has already recognized that a distributed ledger may constitute all or part of a transfer agent’s official master securityholder file. The Commission’s September 2026 proposal takes the next step, proposing to modernize the transfer-agent rules themselves for an environment in which securities records may be maintained electronically and on blockchain infrastructure. It also asks whether the recordkeeping rules adequately support hybrid systems in which on-chain data, such as wallet addresses and share quantities, is linked to off-chain data, such as holder names and addresses, so that a blockchain transfer produces a corresponding change to the official record. Related proposals would replace certificate-era safeguarding requirements with a risk-management framework covering cybersecurity and business continuity, and would clarify requirements for removing restrictive legends and documenting client agreements. The comment period is open. Whatever the final rules, the direction of travel is clear: the ledger is being treated as potential securities infrastructure, not merely as a wrapper around it.

Tokenization Does Not Make Market-Structure and Custody Rules Disappear

Following staff relief for a clearing agency tokenization pilot, rule changes took effect permitting Nasdaq and NYSE markets to accommodate trading of certain securities in tokenized form, and blockchain-based transfer agents have registered with the Commission. These developments also raise an important issue for public companies: tokenization may increasingly become a feature of market infrastructure rather than an issuer-led initiative. As exchange, clearing, and transfer-agent infrastructure develops, issuers may confront tokenized forms of their securities even where tokenization was not part of the issuer’s original capital-markets strategy. Private companies exploring tokenized capitalization tables, fund interests, or secondary liquidity for employees and early investors now have registered service providers to evaluate — and correspondingly less justification for informal arrangements.

Moving from issuance into actual trading is where familiar questions return in unfamiliar form — and where the seams matter most.

• Classification: Are tokenized and conventional shares the same class for corporate and securities law purposes?
• Best execution: How do order-protection and best-execution requirements operate when the same security trades in two formats?
• Custody: For broker-dealer custody purposes, who has possession and control when the asset sits in a wallet?
• Transfer restrictions: How do negotiated transfer restrictions and legends function against a token that can move peer-to-peer in seconds and across borders?
• Settlement: Where does settlement become final, and who bears the risk before that point? The hardest problems arise at the seams between blockchain systems and traditional market plumbing, not within either one in isolation.

What Companies Should Be Thinking About Now

Companies considering tokenization should resolve five questions early:

1. what legal interest the token represents;
2. which record constitutes the authoritative ownership record;
3. who performs transfer agent and custody functions;
4. how on-chain transfers interact with off-chain records; and
5. how the asset will actually trade and settle. These architectural decisions are not implementation details — they drive the regulatory consequences of the structure.

On April 15, 2026, the European Data Protection Board (EDPB) published its long-awaited draft Guidelines 1/2026 on the processing of personal data for scientific research purposes (the Guidelines), marking the most comprehensive regulatory statement to date on how the GDPR applies to scientific research activities.

For organizations in the life sciences sector, the Guidelines address long-standing areas of uncertainty — particularly around secondary use of data, broad consent, and transparency obligations — while signaling a clear shift toward stricter expectations on governance, purpose limitation, and accountability. The EDPB’s objective is clear: to promote a more consistent and workable framework across the EU, while maintaining robust safeguards. The public consultation closed on June 25, 2026, and the Guidelines remain in draft pending adoption of a final version.

Against a backdrop of executive orders targeting diversity, equity, and inclusion (DEI) programs, anti-DEI shareholder proposals, and the post-SFFA v. Harvard legal landscape, companies face growing pressure to reassess their DEI initiatives. To navigate this environment, companies should assess their risk tolerance relative to peers and consider a comprehensive audit of existing DEI policies with counsel. They should also tie their programs to demonstrable business rationale, understand the perspectives of key stakeholders through surveys and listening sessions, maintain a consistent tone at the top, and establish a response team for potential public scrutiny, while monitoring evolving regulatory guidance.

In June 2025, the U.S. Supreme Court upheld a Texas law requiring websites where more than one-third of content is sexually explicit to verify users’ ages before granting access. The critical legal question was which First Amendment standard to apply. The Court chose the more lenient intermediate scrutiny rather than the strict scrutiny used to strike down similar federal laws in the early 2000s, reasoning that the law targets distribution of obscene content to minors and only incidentally burdens adults’ access to protected speech. The three-justice dissent argued strict scrutiny should apply and warned of a chilling effect on lawful speech. The ruling’s reach extends beyond traditional adult content sites to any platform (including social media) if one-third or more of the platform’s content could be deemed harmful to minors. With nearly half of U.S. states having already enacted similar laws, the decision is expected to accelerate that legislative trend. Privacy advocates have separately warned that mandatory age verification forces users to submit sensitive personal identification, raising data breach risks and potentially threatening the First Amendment right to browse the internet anonymously.

Texas has rapidly emerged as one of the nation’s most aggressive data privacy regulators, surpassing California and New York in the breadth and enforcement intensity of its privacy regime. Over the past two years, the state has enacted a comprehensive suite of laws covering consumer and children’s data privacy, artificial intelligence, and data brokers, and its attorney general has secured multi-billion-dollar settlements against major technology companies for alleged violations. While the federal government and many other states have taken a lighter touch on privacy and AI regulation, Texas is leading the charge with active enforcement that companies operating in the state or collecting data from Texas residents cannot afford to ignore.

On July 23, 2025, the Trump administration released its AI Action Plan, aimed at ensuring U.S. global dominance in artificial intelligence, outlining 90 federal policy positions across three pillars: Accelerating Innovation, Building American AI Infrastructure, and Leading in International Diplomacy and Security. The plan calls for streamlining regulatory pathways, expanding public-private partnerships, increasing federal AI research investment, and promoting open-source AI models as potential global standards, while simultaneously encouraging export of the full “American AI Technology Stack” to allied nations as a geopolitical strategy. Three accompanying executive orders promote export of American AI technology, streamline federal data center permitting, and mandate adoption of ideologically neutral “Unbiased AI Principles” in federal procurement. The Action Plan emphasizes deregulation at the federal level and conditions federal discretionary funding on states’ AI regulatory environments, reflecting growing tension between the administration’s national AI framework and state-level regulatory efforts.

The UK’s Medicines and Healthcare products Regulatory Agency moved on several fronts in the first half of 2026, with the intention of developing a more innovation-friendly and AI-aware regime. The developments include a landmark program deepening regulatory cooperation between the UK and the United States, a regulatory “sandbox” for AI used as a medical device, meaning a supervised space where companies can test products with the regulator before full authorization, an evidence-gathering exercise on how AI in healthcare should be regulated, a consultation on a draft Rare Disease Therapies Regulatory Framework, and the largest package of UK clinical trial reforms in more than twenty years. For a founder building AI-enabled diagnostics, software, or therapeutics, this changes both the route to market and its timing: earlier regulator engagement is becoming possible, trial rules are being streamlined, and closer UK-US alignment may reduce duplicated work across the two markets.

President Trump’s December 11, 2025 Executive Order aims to protect American AI innovation from what it characterizes as overly burdensome state AI laws by creating a Department of Justice AI Litigation Task Force to identify and challenge problematic state regulations, conditioning federal Broadband Equity, Access, and Deployment (BEAD) Program funding on state AI policy alignment, directing the Federal Communications Commission (FCC) to consider preemptive federal AI reporting standards, and instructing the Federal Trade Commission (FTC) to clarify how Section 5 of the FTC Act applies to AI in ways that could preempt conflicting state laws. The Executive Order stops short of imposing the broad 10-year moratorium on state AI laws that was previously proposed in legislation, instead taking a more targeted approach that carves out state authority over child safety, AI infrastructure, and government AI procurement. The Executive Order also calls on Congress to enact a uniform federal AI framework.

On June 22, 2026, the White House issued two executive orders on quantum technology: “Ushering in the Next Frontier of Quantum Innovation” and “Securing the Nation Against Advanced Cryptographic Attacks.” Quantum computers can solve certain problems far faster than ordinary computers, which opens up new commercial possibilities but also threatens the encryption that protects most online communication and stored data today. The orders push federal agencies past pure research toward deployment, commercialization, workforce building, and national security uses.