Data & Privacy

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.

On July 7, 2026, the European Data Protection Board (the “EDPB”) released draft Guidelines 02/2026 on Anonymisation. Once final, these guidelines will replace the former Article 29 Working Party’s Opinion 05/2014 on Anonymisation Techniques and reset how companies decide whether data has been anonymised sufficiently to fall outside the EU’s General Data Protection Regulation. The draft adopts a “relative” view of identifiability, following the EU Court of Justice’s decision in EDPS v. SRB: whether data counts as personal depends on who holds it and what that holder could realistically do to re-identify someone. The same dataset can therefore be personal data in one company’s hands and anonymous in another’s.

For a startup, this matters most where anonymised data is doing real work, such as training or fine-tuning AI models, sharing research datasets with partners, or building analytics products. Labelling data “anonymised” is not a one-time conclusion; it depends on context, recipient, and available re-identification tools. The guidelines are in draft form and open for public consultation until October 30, 2026, after which the EDPB will consider feedback and adopt a final version.

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 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.

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.

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.

South Carolina’s new Age-Appropriate Design Code Act is shaping up to be one of the most demanding privacy laws in the country for companies whose products can be reached by people under 18. It pairs prescriptive “privacy by design” requirements, meaning child-safety and data-minimization choices have to be built into the product itself rather than bolted on later, with limits on targeted advertising to minors. It also requires parental tools that let a parent manage a minor’s account settings, restrict purchases, see time spent, and set time limits.

Two features stand out for small teams. First, covered services must obtain an annual review by an independent outside auditor and file that report with the South Carolina Attorney General, and those reports are posted publicly. Second, penalties are unusually aggressive and can reach individual officers and employees personally where violations are willful and wanton.

Generative artificial intelligence (GenAI) tools are becoming increasingly utilized by employees in the workplace to enhance productivity. However, employees are frequently leveraging these tools without formal approval or oversight. While the benefits of GenAI tools may be substantial, use of GenAI may pose significant risks to organizations, including legal liabilities, data breaches, and reputational damage. This article explores certain key concerns associated with employee use of GenAI tools and essential components of an AI use policy.

Understanding the Risks

Data Privacy and Security

One of the most significant risks with the use of GenAI is that information entered into prompts or uploaded to common GenAI platforms may reveal to the AI tool sensitive or confidential company information or information about clients or customers, including information the company may have contractually agreed to keep confidential. Similar risks exist with inputs of personally identifiable information, such as email addresses or other identifiers. Typically, on publicly-available versions of such tools, data that is entered is used to train the AI or may otherwise be stored or used by the AI tool developer.

To mitigate risks of exposing potentially sensitive company data or potentially breaching customer confidentiality agreements, companies may want to consider purchasing enterprise versions of GenAI tools that operate in a closed-loop system, meaning the developer is not permitted to use or store inputs for purposes other than to provide the GenAI tool. Companies may also want to have an approved list of AI tools, which might include enterprise versions of common GenAI tools. Also helpful in mitigating risks is employee training and raising awareness of these risks.

Another significant and emerging risk is that GenAI tools are increasingly being used as a tool for cybercriminals, including through the orchestration of complex injection attacks that exploit the fact that these tools do not think, but rather, simply follow instructions. For example, attackers can embed instructions in publicly available links that, when uploaded to the GenAI tool, tell the tool to maliciously attack the host system and override instructions. Alternatively, the embedded instructions in public links could produce PDFs or other outputs that themselves contain malicious code. Companies should consult with their security team about the risks unique to their business and implement recommended controls if applicable. In addition, companies may want to consider working with external cybersecurity training providers to ensure their materials include coverage of AI tool injection attacks in employee training modules.

Intellectual Property Concerns

Companies should also seriously consider the significant intellectual property (IP) risks posed by employees’ use of GenAI tools. These IP risks arise from two factors: (1) the material used as “input” to train the AI models; and (2) the material generated by the AI model as “output”. AI models are frequently trained on vast datasets which include copyrighted material used without the author’s permission. Furthermore, use of such copyrighted material may cause the GenAI tool to generate outputs that replicate or closely resemble the copyrighted material, further exposing the user to potential copyright infringement claims.

Another IP risk associated with employees’ use of GenAI tools is the lack of IP protection for works and innovations created using GenAI. In the United States, works and innovations created solely by AI without human contribution are not eligible for copyright or patent protection. Without such protections, a company’s competitive advantage may fall into the public domain, where anyone can use it for free.

Use of GenAI may also compromise trade secret protection of a company’s proprietary information. If an employee inputs confidential proprietary information into GenAI tools without proper contractual protections, such information may be used to train the underlying AI models. As noted above, GenAI tools may generate outputs that replicate or closely resemble the inputs, in this case, the proprietary information, potentially exposing the trade secret to other users and compromising the company’s trade secret protection of the information.

Hallucinations and Bias

AI systems, particularly generative models, can produce outputs that appear plausible but are factually incorrect or nonsensical, a phenomenon known as “hallucinations.” These inaccuracies can lead to misguided business decisions, flawed reports, and erosion of trust in AI tools. For instance, an AI-generated report might cite non-existent studies or fabricate data, potentially leading to compliance issues or reputational damage.

Shadow AI Usage

Employees may use unauthorized AI tools (sometimes referred to as “shadow AI”) without the knowledge or approval of IT departments. This practice can result in data security breaches, compliance violations, and loss of control over sensitive information. For example, inputting confidential company data into unvetted AI platforms could inadvertently expose proprietary information. Companies should implement strict policies around shadow AI use and consider limiting access to popular AI tools on workplace computers.

Regulatory Compliance Challenges

The rapidly evolving landscape of AI regulations, such as the European Union’s AI Act, presents compliance challenges for organizations. Failure to adhere to these regulations can result in legal penalties and hindered innovation. Organizations must stay informed about regulatory changes and implement robust governance frameworks to ensure compliance.

DO’S & DON’TS:

Legal Compliance & Policy Design

DO:

• Establish a clear, tailored AI usage policy in collaboration with legal, IT, and compliance teams.
• Define “AI” in plain language, and specify which tools are approved.
• Include a mission or purpose statement to ground the policy in transparency, fairness, and innovation.
• Update the policy regularly to reflect evolving laws (e.g., EU AI Act, state legislation like Colorado’s).

DON’T:

• Don’t use one-size-fits-all policy templates. Each policy should reflect specific business risks and use cases.
• Don’t treat the AI policy as a one-time exercise. Legal exposure grows without active maintenance.

Data Security & Confidentiality

DO:

• Prohibit inputting sensitive, proprietary, or customer data into public AI tools unless specifically authorized.
• Include clear confidentiality provisions related to AI use in employee and vendor contracts.
• Conduct vendor due diligence to confirm AI platforms’ compliance with privacy laws and data handling standards.
• Consider using enterprise versions of AI tools to keep data inputs confidential.

DON’T:

• Don’t assume that AI tools won’t retain user input; review each tool’s terms of service and retention practices.
• Don’t allow shadow IT (e.g., unauthorized browser plugins, personal-use tools) without approval or audit trails.

Intellectual Property & Ownership

DO:

• Review AI vendor agreements to ensure the vendor has proper rights to use training data and provides adequate protections if infringement claims arise.
• Implement human review processes for AI-generated materials to identify potentially infringing content.
• Document human contributions in connection with the development of AI-assisted works and innovations.

DON’T:

• Don’t allow employees to generate customer-facing or public materials without review for potentially infringing content.
• Don’t use AI models trained on unlicensed data without understanding the risk of potential IP infringement claims.
• Don’t allow employees to input confidential proprietary information into AI tools without proper confidentiality and use restrictions.

Training, Governance, & Oversight

DO:

• Require regular employee training on the capabilities, limits, and risks of AI tools.
• Designate responsible roles (e.g., AI Compliance Lead, Tool Vetting Committee) to oversee implementation and approvals.
• Monitor AI tool usage, audit outputs periodically, and solicit user feedback to improve policies.

DON’T:

• Don’t deploy AI tools enterprise-wide without pilot testing, user education, and risk assessment.
• Don’t assume all departments or roles should have equal access to AI tools — access should be role-specific.

Ethical Use & Bias Prevention

DO:

• Embed fairness, transparency, and accountability principles into the company’s AI governance framework.
• Monitor AI systems for evidence of bias, particularly in HR, marketing, or customer interaction tools.
• Require disclosures when AI is used to create or assist in work product — internally or externally.

DON’T:

• Don’t use generative AI content without fact-checking — misleading outputs could carry reputational or legal costs.
• Don’t let AI output go unlabeled in sensitive contexts (e.g., client deliverables, compliance filings).

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 7, 2026, the European Data Protection Board published draft guidelines on web scraping for generative AI, aimed at giving practical guidance on how EU data protection law applies when personal information is pulled from the open internet to train models. The guidelines cover both organizations that do the scraping themselves, directly or through a contractor, and organizations that license or reuse datasets someone else already scraped. For any company buying a generative AI system trained on internet-sourced data, the guidelines, as drafted, do not shift the data protection responsibility away from the buyer. The EDPB’s public consultation on the draft remains open until October 30, 2026, so the text can still change before it is finalized.

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.