IP, Data Privacy, Cybersecurity

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.

Since August 2, 2026, organizations have been subject to the transparency obligations set out in Article 50 of the EU AI Act. Article 50 introduces transparency requirements for providers and deployers in relation to certain AI system functionalities and use cases that may create transparency risks for individuals. Whilst much of the EU AI Act focusses on obligations on high-risk AI systems, Article 50 obligations may also apply to certain limited-risk systems. As a result, many organizations need to implement governance, disclosure, and content-labelling measures to ensure users are appropriately informed about the use of certain AI systems and AI-generated content.

Importantly, these obligations were largely unaffected by the implementation delays contained in the Digital Omnibus on AI (Regulation (EU) 2026/1744), except for the marking obligations contained in Article 50(2) of the EU AI Act, i.e. relating to AI systems that generate synthetic audio, image, video, or text content and that were placed on the market before August 2, 2026. Following the agreement reached between the Council and the European Parliament on May 7, 2026 (see the European Parliament procedure record), and formally adopted on June 29, 2026, the introduction of these marking obligations has been delayed until December 2, 2026.

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.

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.

With AI becoming central to business strategy, the legal questions surrounding it are keeping pace. As companies race to build and deploy AI systems, a critical question is emerging: Do their teams understand the data that powers these tools well enough to manage the risks? For some, the learning curve has come with real legal consequences.

Two categories of AI data sit at the center of these questions: training data and grounding data. Each raises meaningfully different legal and contractual concerns. For organizations working with AI developers and building their own models, understanding the distinction is an increasingly important part of getting AI right.

Training Data: The Textbook

Training data refers to the large datasets used during the development of an AI model. During training, the model analyzes large quantities of information, such as text, images, or software code, to identify patterns that allow the model to generate outputs when responding to prompts.

Think of training data as a student’s textbook.

Once students have read a textbook, they don’t carry it around to solve problems.

Training datasets can originate from a variety of sources, including:

• publicly available internet content, such as websites or online forums;
• digitized books, academic journals, and research publications;
• open-source software repositories;
• commercially licensed databases; and
• proprietary enterprise datasets (typically owned by the developer or provided to the developer by an external partner, customer, or collaborator).

Because these datasets often contain copyrighted or proprietary materials, and potentially personal information or other regulated data, the legal rights associated with training data can vary significantly. In some situations, the AI developer may own or curate the dataset itself (including, with respect to curation, to mitigate regulatory risks by using deidentified or synthetic data). In others, the underlying materials may remain owned by third-party publishers, content creators or owners, or database providers (and may still include data subject to various regulatory regimes).

Companies developing AI models internally may also incorporate proprietary operational data, customer information, or research datasets. When external collaborators are involved, training data may be governed by contractual agreements that define ownership, permitted uses, and rights to derivative models and datasets.

Importantly, training data is typically used during the model development phase. Once the model has been trained, the system generally no longer needs direct access to the underlying training dataset in order to function. The distinction can become relevant when licensing arrangements change or when organizations reevaluate their data governance strategies after a model has already been deployed.

Grounding Data: The Exam Fact Pattern

Grounding data serves a different purpose. Rather than teaching the model general patterns, grounding data provides contextual information at the time a model generates a response to a prompt. Organizations frequently use grounding to ensure that AI systems reference authoritative or up-to-date sources when producing outputs.

Think of grounding data as an exam fact pattern with specific facts and context.

Grounding data can include:

• internal company policies or compliance manuals (e.g., an employee handbook retrieved to answer an HR query);
• corporate knowledge bases and document repositories (e.g., past deal memos surfaced during contract negotiation);
• regulatory guidance or industry standards (e.g., Food and Drug Administration guidance retrieved to support a compliance question);
• proprietary databases (e.g., a licensed case law database queried at inference time); and
• user-provided documents or uploaded files (e.g., a contract the user uploads and asks the AI tool to summarize).

To make this concrete: When an attorney asks an AI tool, “What are the notice requirements under this contract?” the user input is that question. The grounding data is what the system automatically retrieves behind the scenes to answer it (e.g., the specific contract, the firm’s internal playbook, a relevant regulatory database). The user does not see that retrieval, but it drives the response.

A common implementation approach for grounding data is retrieval-augmented generation (RAG), where an AI system retrieves relevant documents and information from a database or search index and incorporates those materials into its response.

For businesses deploying AI tools internally, grounding can play an important role in maintaining guardrails around model outputs. By referencing trusted materials, grounding mechanisms can help improve model accuracy and reduce the likelihood that unsupported or misleading responses are generated.

From a legal perspective, grounding data presents a distinct set of considerations because it is actively retrieved and potentially reproduced at the time of each query rather than absorbed into the model’s parameters during the development phase. This distinction also has practical consequences postdeployment: A trained model generally continues to function even if access to its training data is later lost or a license expires, because the learned patterns are encoded in the model’s weights. Grounding data, by contrast, must remain continuously accessible, and loss of access or limitations on access to support risk management needs (e.g., cybersecurity, privacy, or other data governance purposes) may directly impair the model’s outputs.

The following table summarizes the key distinctions between training data and grounding data.

Litigation Trends: Focus on Training Data

To date, the most significant AI copyright decisions have focused on training data practices — specifically, whether copying protected works to build or improve a model is authorized and lawful. No court has yet issued a comparable ruling centered on grounding data or RAG systems, though legal risks in that area are building, as discussed below.

In Thomson Reuters Enterprise Centre GmbH v. ROSS Intelligence Inc., Thomson Reuters, the owner of Westlaw, sued ROSS Intelligence, a developer of an AI-powered legal research tool, alleging that ROSS used Westlaw headnotes as training data. ROSS argued that its use of the headnotes to train its model constituted fair use because the system learned from the material without reproducing it (i.e., without including the material in outputs). The U.S. District Court for the District of Delaware rejected that argument (in part) and found infringement, emphasizing that the use of proprietary, curated content to train a competing product weighed heavily against fair use. The decision suggests that training on copyright-protected materials, particularly where the resulting system functions as a market substitute, can give rise to copyright infringement risk even if the model does not use the original content in its output. Notably, the Court explicitly mentioned in its holding that only nongenerative AI was at issue in this case.¹

Two decisions issued in June 2025 by the U.S. District Court for the Northern District of California reached different conclusions in cases involving generative AI and together illustrate how fact-specific the fair-use analysis has become.

In Kadrey v. Meta Platforms, a group of authors sued Meta alleging that training its LLaMA large language model on copyrighted books constituted infringement. The Court found for Meta, concluding that the training use was a highly transformative fair use. The Court also found that the plaintiffs had failed to develop sufficient evidence of market harm (one of the central fair-use factors) and analyzed the copying and the training as a single, unified act rather than as separate instances of reproduction and use. The decision is notable, but its reach is limited: The Court declined to issue a broad ruling that AI training on copyrighted materials is categorically fair use, and the outcome turned heavily on the specific evidence presented (or lack thereof).²

In Bartz v. Anthropic, also decided in June 2025, the Court drew a sharper line based on how training copies were obtained. The Court found that training the Claude model on lawfully acquired or purchased books constituted fair use but declined to extend that protection to a central library Anthropic had compiled from pirated sources. The distinction (fair use for legally obtained copies, no fair use for pirated ones) has practical implications for how AI developers source and document their training data. The case settled for approximately US$1.5 billion following the ruling.³

Taken together, ROSS, Kadrey, and Bartz reflect a landscape in which outcomes turn on particular facts: what kind of content was used, how it was obtained, whether the resulting system competes with the original market, and what evidence of harm was presented. No appellate court has yet weighed in on the merits of AI training and fair use, meaning none of these decisions are binding precedent beyond its own jurisdiction. Significant cases remain in active litigation, including The New York Times Co. v. OpenAI and Microsoft in the Southern District of New York (the first major generative AI case brought by a news organization) and Disney v. Midjourney in the Central District of California, both of which are pending.^{4, 5} The appellate trajectory of ROSS, which is on interlocutory appeal to the Third Circuit, may also provide the first appellate guidance on these questions. For now, the law remains unsettled, and companies with significant exposure should monitor developments closely.

RAG Systems, Scraping, and Emerging Risks

As AI systems increasingly rely on grounding techniques such as RAG, new legal issues are beginning to emerge. Many RAG implementations retrieve information from external sources in real time, which may involve web scraping, application programming interface (API) access, or database queries. These practices raise potential risks that differ from traditional training-data disputes. That distinction drives the broader IP risk profile:

• Terms of Service — RAG systems that query external sources in real time may exceed what a website or API permits.
  
• Circumvention of access restrictions — retrieving content from paywalled or restricted sources can trigger additional liability beyond copyright (e.g., risks related to computer crime, privacy, or cybersecurity laws).
  
• Output reproduction — because retrieved text may appear verbatim in model responses, copyright infringement risk arises at the moment of each query, not just during development.
  
• Contractual limitations on database usage — licensed databases often do not contemplate AI systems incorporating their content into generated outputs, creating potential breach risk.

Organizations deploying RAG-based systems should therefore evaluate how their AI models retrieve and present external information. Practical risk-mitigation steps may include prioritizing vetted and licensed data sources, reviewing website terms before scraping content, and implementing technical safeguards that limit the amount of retrieved material incorporated into model outputs.

Contractual Considerations

Understanding the legal distinctions between training data and grounding data has direct implications for how organizations should structure their AI-related agreements. The two data types raise different contractual concerns, and companies entering into AI development or deployment arrangements should evaluate each category separately.

Training Data Agreements

When training data is sourced from third parties, whether licensed databases, research partners, or content providers, agreements should address several core issues. First, ownership and chain of title should be clearly defined: who owns or controls each component of the dataset, and who owns any derived datasets, curated subsets, or annotation outputs produced during the development process. This is particularly important where multiple parties contribute data or where the dataset is assembled by a third-party curator.

Second, the scope of the license should be carefully defined. Agreements should specify which model versions may be trained on the data, whether use is limited to internal deployment or extends to commercial products, and whether fine-tuned derivatives of the trained model fall within the license. Restricting sublicensing of training data to third parties is also worth addressing explicitly, particularly in multiparty development arrangements.

Third, agreements should address model ownership. Whether the trained model constitutes a derivative work of the training data (and if so, who owns it) is a contested question. Parties should allocate model ownership explicitly rather than leaving it to inference. This can be a significant negotiation point, particularly where a data provider is contributing proprietary content that materially shapes the model’s capabilities.

Finally, training data agreements should include representations and warranties from the data provider confirming that it has sufficient rights to license the dataset and that the licensee’s use of the dataset as permitted will not infringe third-party intellectual property rights. Indemnification provisions covering third-party infringement claims arising from the training data are increasingly standard in well-negotiated AI development agreements. Companies should also consider what happens when a training data license expires or is terminated postdeployment. As discussed, a trained model typically continues to function even without ongoing access to its training data, but audit rights and data deletion obligations may still apply and should be addressed in advance.

Grounding Data Agreements

Grounding data raises a distinct set of contractual issues, driven by the fact that grounding data likely must remain accessible throughout the life of the deployment. Unlike training data, which is consumed during the build phase, grounding data is actively retrieved on each query, making ongoing access a functional dependency rather than a historical one.

License agreements covering grounding data should be drafted to cover runtime retrieval specifically, not just one-time processing or storage. Traditional database licenses often do not contemplate an AI system incorporating retrieved content into generated outputs, a use that is meaningfully different from a human reading a record. Companies should audit their existing data licenses to confirm they extend to AI inference use before deploying RAG-based systems that draw on licensed sources.

Because grounding data may be directly reproduced in AI outputs, agreements should also address output ownership and attribution: whether the data provider retains any rights in AI-generated responses that incorporate its content and whether attribution or citation requirements apply. This is an emerging area, and parties should not assume that silence in a license means no rights are retained.

Where enterprise grounding data contains sensitive business information or trade secrets, confidentiality and data security provisions are essential. Agreements with AI vendors should specify how grounding data is stored and accessed, who within the vendor’s organization can retrieve it, and whether the vendor may use the data to train or improve its own models. This last point is often buried in vendor terms of service and deserves careful review.

Finally, because loss of access to grounding data can immediately impair a live AI system, agreements should include provisions addressing continuity and license termination. This may include uptime and availability service-level agreements for critical data sources, step-in rights if access is revoked, and data portability provisions allowing the organization to migrate to alternative sources without disruption. The operational stakes here are often higher than with training data, where the model continues to function even after the underlying data is no longer accessible.

Key Takeaways for Businesses

• For companies adopting AI technologies, data governance is a critical component of responsible AI deployment.
  
• Training data raises questions about IP rights, licensing, and model development practices, as well as privacy, regulated data, and cybersecurity-related risks.
  
• Grounding data affects how AI systems operate in real time and how organizations maintain reliability and control over model outputs and may present similar privacy, regulated data, and cybersecurity risks.
  
• As AI adoption continues to expand across industries, companies that carefully evaluate both aspects of the AI data pipeline will be better positioned to capture the technology’s benefits while minimizing legal and operational risk.

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).

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.

Following an industry challenge to Texas Senate Bill 140’s new text marketing registration requirements, the Texas Attorney General submitted a detailed interpretation of the law agreeing that the registration requirements were not intended to apply to businesses that obtain prior consumer consent before sending marketing text messages. As part of a subsequent settlement, the Texas Secretary of State posted guidance on its website confirming that consent-based text senders are not required to complete the telemarketing registration process and agreed to formally request a published AG opinion reinforcing this interpretation. While this clarification significantly reduces the registration risk for businesses with proper consent practices, some litigation risk remains.  Plaintiffs’ attorneys may still attempt to assert the law’s private right of action and other requirements of the law will continue to apply, such as quiet hours restrictions.

Texas amended its telephone solicitation and telemarketing law (known as the “mini-TCPA”) through Senate Bill 140, effective September 1, 2025, to require businesses sending marketing text messages to Texans to annually register with the Secretary of State, pay a registration fee, post a $10,000 security deposit, and make detailed disclosures about their business and planned solicitations. The law expands the definition of “telephone solicitation” to include text messages and creates de facto violations of the Texas Deceptive Trade Practices Act (which carries significant penalties including treble damages and a private right of action) for violations of existing text messaging consent and no-call list requirements. Several exemptions may limit applicability for established businesses, including carveouts for publicly traded companies, supervised financial institutions, nonprofits, and entities that have operated under the same name for at least two years and are messaging current or former customers, though the scope of the customer exemption is somewhat ambiguous.

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.

England’s High Court has issued the UK’s first ruling in a copyright fight against an AI developer with an outcome that favors the AI industry. In Getty Images v. Stability AI, Getty (which licenses photos and video) alleged that Stability scraped millions of its images to train the Stable Diffusion image generator. During trial Getty dropped its main copyright claim because it could not show the training actually happened in the UK. Copyright is territorial, so no act in the UK means no UK infringement.

That left a narrower question: whether importing the already-trained model into the UK made it an “infringing copy” under the Copyright, Designs and Patents Act 1988. The court said no. A model’s weights are just numbers derived from statistics; they do not store or contain the underlying images, so supplying the model is not secondary infringement. Getty won only on a historic and limited trademark point about its marks appearing in outputs. The decision settles little about whether UK-based training would infringe, and Getty may appeal.

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.