Artificial intelligence is moving into government contract performance.

  • AI now supports scheduling, logistics, and production planning on federal contracts.

Defense contractors are asking a critical question.

  • Does using AI on a Navy or other government contract give the government rights to your proprietary systems, methods, or data?
  • The short answer is no. The real risk lies elsewhere.

Vague AI transparency and explainability clauses can quietly expand disclosure obligations.

  • This contract language can erode trade secret protections that have existed for decades.

This video explains how government IP rights apply to AI-assisted performance and identifies the exposure points that general counsel need to know about.

  • Key actions include defining deliverables precisely, managing explainability requests without revealing proprietary logic, and treating AI workflows as intellectual property assets.
#GovernmentContracts #ArtificialIntelligence #NavyContracts #ShipRepair #DefenseIndustry #FederalContracts #TradeSecrets #AICompliance #industriallawyer #insurancelawyer #commericallitigation #privacylaw #datasecuritylaw #whitecollardefense #defenselaw #classactionlawsuits #AIlawsuits #AIdatabreach

Artificial intelligence is reshaping how lawyers research, draft, and advise.

But AI has introduced two legal risks that users cannot ignore.

  • Courts are sanctioning attorneys for AI-generated errors in court filings.
  • And a February 2026 federal decision held that a client’s independent use of a public AI chatbot was not protected by attorney-client privilege.

This video explains both risks, outlines the decisions behind them, and identifies steps in-house counsel can take now, including AI-use policies and guidance for legal departments.


#ArtificialIntelligence #LegalEthics #AttorneyClientPrivilege #AILaw #LegalTechnology #industriallawyer #insurancelawyer #commericallitigation #privacylaw #datasecuritylaw #whitecollardefense #defenselaw #classactionlawsuits #AIlawsuits #AIdatabreach

If you are a Navy contractor, ship repair company, or defense supplier wondering whether using AI puts trade secrets at risk, you are asking exactly the right question. I see why this concern is growing. Federal agencies now operate under formal AI governance and acquisition guidance, which means contractors should expect more questions about oversight, documentation, and how AI-assisted decisions are made.

My short answer is this: AI itself does not automatically hand your trade secrets to the government. The real risk usually comes from contract language, deliverable definitions, disclosure obligations, vendor terms, data handling, and how your team responds when the government asks for an explanation. That is where contractors can lose control of valuable methods without realizing it until the project is already underway.

In my view, the better question is not simply whether using AI puts trade secrets at risk. The better question is what part of your workflow could turn an internal advantage into something you are forced to disclose, deliver, or defend. When I look at this issue, I focus on the contract first, then the data, then the process.

#GovernmentContracts #ArtificialIntelligence #NavyContracts #ShipRepair #DefenseIndustry #FederalContracts #TradeSecrets #AICompliance #industriallawyer #insurancelawyer #commericallitigation #privacylaw #datasecuritylaw #whitecollardefense #defenselaw #classactionlawsuits #AIlawsuits #AIdatabreach

Does using AI put trade secrets at risk by itself?

Trade secret law can protect valuable business and technical information, including internal methods, processes, programs, planning systems, and engineering know-how, as long as that information is actually treated as confidential. That can include scheduling logic, labor forecasting assumptions, optimization rules, and internal AI-supported workflows that give a contractor a competitive advantage.

The key point is that using AI alone does not automatically mean those protections disappear. If a contractor uses AI as an internal tool to help perform work, that does not automatically transfer the underlying model, logic, or proprietary process to the government. The more important question is what the contract requires the contractor to deliver or explain. If the contract is vague about AI outputs, transparency, or explainability, that is where the real risk begins. In other words, the problem is usually not the use of AI itself, but rather that it is unclear contract language that can blur the line between the final work product and the protected internal methods behind it.

Where the real exposure begins

The real exposure usually starts with vague drafting. If a contract requires a schedule, a forecast, a recommendation, or a report, that sounds straightforward. But once a clause starts using broad phrases like AI outputs, explainability, transparency, validation materials, model documentation, or auditable decision support, the scope can widen quickly. Federal AI acquisition and governance memoranda have pushed agencies to strengthen risk management and visibility into AI systems, so contractors should expect more scrutiny around how AI-assisted outputs are generated and controlled.

Now that doesn’t mean the government automatically owns your internal methods. However, sloppy definitions can create leverage for broader disclosure requests. When explainability is undefined, a request for an explanation can turn into pressure to reveal assumptions, weighting, workflow logic, source inputs, or other confidential details that give your company its advantage. That is the moment when using AI becomes a practical contract problem instead of a theoretical one.

Why this matters so much on Navy work

Navy and ship repair work is especially sensitive because competitive advantage often lives inside the process rather than the final paper you hand over.

A schedule may look simple on the surface. But behind that schedule may be years of institutional knowledge about drydock congestion, supplier slippage, rework patterns, labor efficiency, overtime impact, sequencing risk, and how to compress an availability without creating failure elsewhere. If an internal AI-assisted tool helps your team turn that experience into better performance, the trade secret value often sits in the method, not just the final output. Under trade secret law, that kind of technical and business process information can qualify for protection when properly safeguarded.

That is why I don’t tell contractors to stop using AI. I tell them to define the boundary between the deliverable and the engine that produced it. If that boundary is not clearly drawn, using AI can shift from a smart business question to an expensive dispute.

A real-world example contractors should recognize

Imagine a ship repair contractor managing overlapping availabilities across limited drydock space. The company uses an internal AI-assisted scheduling tool trained on its own proprietary historical labor patterns, supplier reliability data, rework history, and sequencing behavior. The contract requires the contractor to deliver an updated schedule, and that is exactly what the contractor provides.

At that stage, the issue is not simply that AI was used. The more important question is whether the contract clearly defines what must be delivered and what must be explained. Problems can start when performance pressure builds, and the government wants to know why one availability was sequenced ahead of another. If the contract includes broad or unclear language about AI transparency or explainability, the discussion can quickly move beyond the final schedule and into assumptions, scenario testing, internal weighting, or decision-making logic that the contractor never intended to disclose.

That is how this risk often develops in practice. It isn’t because AI automatically strips away protection. Unclear contract language can blur the line between the final work product and the proprietary methods behind it.

What general counsel and contract teams should do now

1. Map where AI is actually being used

You cannot protect what you have not identified. Many companies discover too late that program teams are already using AI in scheduling, forecasting, logistics, quoting, maintenance planning, document review, or reporting. Start with visibility. Determine what tools are in use, what data they touch, and whether any output is flowing into a contract deliverable. Recent federal AI guidance makes visibility and risk management a practical necessity, not just a governance exercise.

2. Define deliverables with precision

Be explicit about what the government receives and what remains internal. If your AI system is merely an internal support tool, the contract should say so where possible. If only the final schedule, report, or recommendation is deliverable, define that clearly.

3. Control explainability before it controls you

I encourage contractors to prepare an explanation framework that describes outcomes without disclosing proprietary logic. That may include business justification, process summaries, validation results, or performance metrics that answer the government’s concern without exposing the underlying method. In many disputes, the problem is not that an explanation was required. The problem is that no one defined the safe level of explanation in advance.

4. Mark and segregate proprietary data

If your company is going to assert limitations, it needs disciplined marking, clean documentation, and a process to support those assertions. Also, separate government data from proprietary training data wherever possible. If you blend everything together carelessly, you invite arguments later about rights, access, and scope.

5. Review vendor terms before adoption, not after a dispute

Your internal AI tool may not be as internal as you think if the vendor retains broad rights, stores prompts indefinitely, uses input for training, or imposes weak confidentiality terms. That is a trade secret issue and a contract risk issue.

6. Train program managers to stop oversharing

A surprising number of disclosure problems begin in meetings, status calls, slide decks, white papers, or well-intentioned technical explanations. If your team does not know the line between explaining a result and revealing a protected method, it’s much more likely that using AI can put trade secrets at risk. Internal training matters. So does a clear escalation path for disclosure questions.

The questions contractors are already asking

Does using AI put trade secrets at risk on a government contract?

It can, but not automatically. The main risk comes from what the contract requires you to deliver, disclose, validate, or explain, along with how well you protect secrecy in practice.

If I use AI in scheduling, does the Navy own my model?

Usually not just because you used it. Ownership and license rights depend on the contract, the applicable clauses, the source of development funding, and whether the software or technical data are actually delivered.

What counts as a trade secret in an AI workflow?

Potentially, your training approach, source code, prompts, assumptions, workflows, data curation methods, weighting logic, decision rules, or process architecture, if they derive value from secrecy, and you take reasonable steps to protect them.

Can explainability requirements force disclosure of proprietary logic?

They can if the contract language is broad and your company has not set boundaries around what explanation means. That is why precise drafting matters so much.

What is the safest first step for a contractor using AI today?

Do a contract and workflow review before the next proposal, modification, or deliverable cycle. That is the best time to narrow definitions, strengthen markings, and fix data practices before a dispute starts.

Protect the advantage before the contract gives it away

For contractors, the most useful answer to whether using AI puts trade secrets at risk is this: AI is not the automatic transfer event. Bad definitions, weak controls, careless disclosures, and unmanaged contract language are.

That is why I would treat AI workflows the same way I would treat any other valuable technical edge. Identify them. Protect them. Define them carefully in the contract. Control how they are explained. Keep your proprietary data cleanly separated. And do not assume that a smart internal tool will stay protected if your paperwork and process say otherwise.

If you are working through these issues now, you can review my experience handling complex commercial, technology, and AI related disputes, or contact me directly to talk through your contract language and workflow. Acting early gives you the best chance to protect your rights, preserve your competitive advantage, and avoid turning a useful AI tool into a disclosure problem later.

Introduction and Background

In Feb. 2026, a public-private partnership headed by the U.S. Department of the Treasury concluded an investigative process aimed at strengthening cybersecurity and risk mitigation for AI in the financial services sector. The partnership consisted of executives from over 100 financial institutions, U.S. and international agencies, federal and state financial regulators, and other key stakeholders. One of the partnership’s key deliverables announced at the conclusion of the investigation is the Financial Services AI Risk Management Framework (“Financial Services AI RMF”), which adopts and expands the AI Risk Management Framework provided by the National Institute of Standards and Technology (“NIST Framework”) for specific application to the financial services industry.

Framework Organization

The NIST Framework is organized into four “functions”: Govern, Map, Measure, and Manage. The Financial Services AI RMF adopts the NIST Framework’s functions, but then provides further controls under each function, which are aimed at tailoring the framework to the financial services sector. The Financial Services AI RMF contains 230 controls designed to be scalable and adaptable for financial institutions, including community banks, credit unions, national and multinational banks, insurers, investment firms, and their third-party providers.[1] Implementation of the Financial Services AI RMF is not mandatory; the framework is instead categorized as a tool that is “complementary to existing risk frameworks” and that “synthesizes global standards and supervisory expectations.”[2]

The Financial Services AI RMF consists of four components: 1) an AI adoption stage questionnaire, which businesses can fill out as a starting point to identify their current AI adoption stage; 2) a risk and control matrix, which lists the 230 controls; 3) a user guidebook for control adoption and implementation; and 4) a control objective reference guide, which provides further information on each control, as well as examples of “effective evidence” of implementation.

Key Controls

Below, we highlight and summarize a sample of certain controls that legal counsel can help financial services organizations assess and address:

  • Govern 1.1.1: The organization identifies, monitors, and integrates applicable laws, regulations, contractual obligations, and sector requirements into policies, procedures, and operations.
  • Govern 1.1.3: The organization implements procedures to validate AI system compliance with law, including audits and impact assessments.
  • Govern 6.1.1: The organization establishes processes for evaluating and selecting third-party AI technologies based on criteria that assess security and privacy implications, due diligence, and contracting practices.
  • Govern 1.2.3: The organization develops an AI Acceptable Use Policy.
  • Map 4.1.1: The organization documents processes for identifying, mapping, assessing, and managing potential legal risks associated with the AI systems, including risk related to data privacy, intellectual property, third-party rights, and use of service providers.
  • Map 4.1.3: The organization communicates identified legal risks associated with the AI system, and changes in laws, regulations, and industry standards, to relevant stakeholders.
  • Map 5.2.2: The organization engages with stakeholders to solicit insights and develop action plans that detect, prevent, and mitigate potential risks, costs, or adverse impacts.
  • Measure 2.10.1: The organization conducts an initial examination of the privacy risks associated with AI systems and documents the results. The organization establishes mechanisms for managing risks and incidents, such as data breaches.
  • Measure 2.10.3: The organization establishes procedures for tracking and managing data subject consent, including handling data subject rights requests.
  • Manage 3.1.5: The organization monitors AI risks associated with third-party resources, including monitoring contracts and contract compliance.

The Taft Privacy, Security, and AI team stands ready to assist financial services organizations in implementing the Financial Services AI RMF and otherwise assessing and managing enterprise AI risk.

[1] See a description of the Financial Services AI RMF online here: https://cyberriskinstitute.org/artificial-intelligence-risk-management/.

[2] Id.

SAP is under formal EU antitrust investigation.

  • SAP is well known for using aggressive tactics to limit customers’ support options and creating questionable revenue-generating events.
  • What does this mean for you?

That’s what I discuss in this clip from my upcoming video.

#erpcommunity #erpfailure #saps4hana #oraclecontracts #softwarelawyer #sapservices #saphanacloudplatform #saas #erpcloud #teamtaft #sapcontracts #oraclelawsuit #oraclefailure #oracletermination #saptermination

If you are moving fast with generative tools, the best legal practices when you use AI are not optional. They are what keep a promising brand from turning into a trademark dispute, a false advertising claim, or a costly rebrand.

What are the best legal practices when you use AI for branding?

The best legal practices when you use AI for branding are to keep meaningful human control, clear names and slogans before launch, verify every marketing claim, review vendor terms, document creative decisions, avoid lookalike prompts, and require legal signoff before a campaign goes live. That answer matters because the legal risk is very real. Under Section 43(a) of the Lanham Act, a business can face claims for false designations of origin, misleading descriptions, and advertising that is likely to cause confusion or misrepresent the nature or qualities of goods or services. The FTC also says advertising must be truthful, non-deceptive, and backed by evidence. Essentially, if your AI-assisted branding looks too much like someone else’s mark or makes claims you cannot support, you can still be sued.

Why AI speed creates trademark risk

Most companies do not have a legal framework for AI-assisted branding. Teams prompt a tool, pick the output they like, and move forward. That speed feels efficient, but it can hide major problems.

AI tools can produce names, slogans, logos, and product copy that sound familiar because they are built to predict likely language patterns. In branding, familiarity can be dangerous. A slogan that sounds polished may also sound too close to a competitor’s long-used phrase. A logo concept may feel distinctive inside your design meeting, but look much less original when compared against the market. Product copy may sound persuasive while quietly making claims your business has never tested or documented.

That is why the best legal practices when you use AI should begin before launch, not after someone sends a demand letter.

The law still applies even when AI helped create the brand

Some business leaders assume that if AI generated the first draft, the usual rules are somehow softer. They are not.

The Lanham Act still governs confusing branding and false advertising. The FTC still expects truthful, substantiated claims. That means the questions remain the same for Indianapolis businesses and national brands alike. Will consumers be confused? Is the claim accurate? Can you prove it? If the answer is unclear, you need more review before launch.

7 best legal practices when you use AI to build your brand

1. Keep human ownership and control

The first rule is simple. A person, not a tool, should own the decision-making process. Your team should define brand strategy, choose among outputs, revise what the AI creates, and approve the final result. Human control helps show that the brand reflects business judgment rather than an unexamined machine output.

That approach also fits with broader federal IP guidance. The USPTO has stated that AI systems are tools used by human inventors, not inventors themselves, and the U.S. Copyright Office has explained that protection turns on sufficient human authorship rather than mere prompting alone. Branding is not identical to patent or copyright law, but the practical lesson is the same: human contribution matters.

2. Clear AI-generated names, slogans, logos, and domains

Every serious brand element should go through clearance. That means screening for similar marks, similar slogans, overlapping domains, and marketplace use that could trigger confusion.

Too many teams think AI output is new because it feels new to them. That is not how trademark risk works. The real question is whether your proposed brand conflicts with rights that already exist. Clearance should happen before launch, before ad spend, and before public rollout across multiple channels.

3. Substantiate claims before they go live

This step matters just as much as trademark clearance. If AI writes product claims, performance statements, comparisons, or guarantees, someone needs to verify each one.

The FTC’s rule is direct: ads must be truthful, advertisers must have evidence to back up their claims, and ads cannot be unfair. So if your AI drafted language like “industry leading,” “clinically proven,” “fastest,” or “guaranteed,” your business should pause and ask what evidence supports that wording. If you cannot prove it, do not publish it.

4. Review vendor terms and output rights

Not every AI platform gives you the same protections. Before relying on a tool for core brand development, review who owns the output, what restrictions apply, how the provider handles training data, and whether any indemnity exists.

For enterprise users, this is often negotiable. It is better to address those issues at the contracting stage than after a dispute arises over ownership, confidentiality, or third-party claims.

5. Create an audit trail

Save prompt history, revision notes, internal comments, search results, and the reasons your team selected one option over another. Documentation serves two purposes.

First, it helps prove thoughtful human involvement. Second, it shows your business acted responsibly if a dispute later arises. In litigation, a clean record can matter. It can show you did not blindly copy, did not ignore obvious risk, and did take legal review seriously.

6. Ban lookalike prompting

One of the riskiest habits in AI branding is asking for something “like” a competitor, a market leader, or a famous campaign. That shortcut may feel harmless in a brainstorming session, but it increases the odds that the output will echo protected branding or trade dress.

A better prompt focuses on your own personality, values, audience, and differentiators. Ask for originality, not imitation. This is one of the most practical and best legal practices when you use AI because it reduces confusion risk at the source.

7. Use launch gates before anything goes public

Build a final review checkpoint into the process. No brand element, campaign asset, or AI-drafted claim should go live without a confusion check and legal signoff.

This can be lightweight for lower-risk materials and more formal for major launches. The important point is consistency. A launch gate gives your company one last chance to catch a problem before the public sees it.

A realistic example of how this can unravel

Imagine an Indianapolis software company using AI to generate a new tagline for a product rollout. The internal team loves the result because it sounds polished, modern, and memorable. The marketing department places it on the website, in paid ads, and across sales decks.

A month later, the company receives a demand letter. A competitor has used a very similar tagline for years in the same industry. Now the competitor argues the new campaign is likely to confuse customers and also points to AI-drafted performance claims in the ads that were never substantiated.

This is exactly the kind of situation that can turn a fast launch into a costly legal problem. A basic clearance search, documented human edits, and a claim review checklist might have caught the issue before launch. That risk framework tracks the seven-step structure discussed in your transcript.

Questions I hear from business teams using AI

Can I trademark a name that AI helped me create?

Possibly, but the key issue is not whether AI helped. The real issue is whether the proposed mark is distinctive, available, and lawfully used in commerce without creating confusion with someone else’s rights.

Does using AI protect me if the output copies someone else?

No. If your branding creates confusion or includes misleading claims, the legal exposure stays with the business that used the material.

Do I need a lawyer before I launch an AI-generated slogan?

For a minor internal concept, maybe not immediately. But before a public launch, legal clearance is wise, especially if the slogan will appear in advertising, on packaging, or across multiple states.

What kinds of AI-generated claims are most dangerous?

Comparative claims, performance claims, superiority claims, scientific-sounding claims, and guarantees are all high risk when they are not backed by evidence.

Should my company have a written AI branding policy?

Yes. A written policy helps marketing, legal, leadership, and outside agencies follow the same review process. It also reduces inconsistency, which is where avoidable risk often begins.

A practical policy your team can use now

If your company is serious about AI-assisted branding, your policy should at least require the following:

  • A designated owner for brand decisions
  • Pre-launch trademark and slogan clearance
  • Review of all factual and performance claims
  • Approval of vendor terms for AI tools
  • Documentation of prompts, edits, and rationale
  • A prohibition on lookalike prompting
  • A final legal review before publication

That framework is not burdensome. It is efficient risk control. In many cases, it is the difference between scaling a brand confidently and backing up under pressure.

Protect the upside before the risk gets expensive

AI can absolutely help your business move faster. But speed without legal discipline is not a growth strategy. It is exposure.

The best legal practices when you use AI allow you to keep the benefits of speed while protecting trademark value, advertising accuracy, and long-term brand ownership. When your process includes human control, clearance, substantiation, documentation, and launch review, you give your business a much stronger foundation for growth.

If you are building or refreshing a brand with AI, now is the right time to review your process. You can learn more about my experience in commercial litigation, privacy, security, and artificial intelligence matters, explore my Taft profile and contact information, and read Taft’s related guidance on AI-powered fraud and business safeguards.

The Financial Industry Regulatory Authority (“FINRA”) and the U.S. Department of the Treasury (“Treasury”) (as part of a public-private partnership) have recently issued guidance regarding the use of AI by the financial services industry. This alert summarizes certain AI-related updates from the 2026 FINRA Annual Regulatory Oversight Report (the “Report”), and the Treasury partnership’s recently published AI Lexicon and Financial Services AI Risk Management Framework.

FINRA

FINRA’s 2026 Report contains a new section specifically devoted to generative AI (“GenAI”). The Report clarifies that “FINRA’s rules… and the securities laws more generally, continue to apply when firms use GenAI or similar technologies in the course of their businesses, just as they apply when firms use any other technology or tool.”1 The Report suggests that existing rules regarding supervision, communications, recordkeeping, and fair dealing may apply to uses of GenAI by securities broker-dealers.2

The Report provides recommendations for firms contemplating GenAI solutions. Such recommendations include:

  • “Robust testing of GenAI to understand the capabilities, limitations, and performance of the model. Testing areas to consider include areas such as privacy, integrity, reliability, and accuracy.”
  • “Ongoing monitoring of prompts, responses, and outputs to confirm the GenAI solution continues to perform as expected and results in compliant behavior.”
  • “Approaches to identify and mitigate associated risks, including, but not limited to, accuracy (e.g., hallucinations) and bias.”
  • “Assessing whether the firm’s cybersecurity program appropriately contemplates: risks associated with the firm’s and its third-party vendors’ use of GenAI; and how its technology tools, data provenance, and processes identify how threat actors use AI or GenAI against the firm or its customers.”
  • “Developing supervisory processes to develop and use GenAI at an enterprise level.”
  • “Establishing a supervision, governance, or model risk management framework that establishes clear policies and procedures to develop, implement, use, and monitor GenAI, while maintaining comprehensive documentation throughout.3

In addition to the Report, Treasury has issued guidance that is instructive on the use of AI by financial institutions.

U.S. Department of the Treasury

The U.S. Department of the Treasury recently released two new resources to guide AI use in the financial sector, a shared Artificial Intelligence Lexicon and the Financial Services AI Risk Management Framework (FS AI RMF) as part of the President’s AI Action Plan,4 that focuses on “clear standards, shared understanding, and risk-based governance to ensure artificial intelligence is deployed safely and responsibly.5 “As a part of a public-private partnership, the Treasury new AI guidance resources intended to enable the “secure and resilient”6 development and use of AI across the U.S. financial system. The publications consisted of an “AI Lexicon,”7 promoting a shared AI vocabulary, and the “Financial Services AI Risk Management Framework,” (the “Framework”) which modifies the NIST AI Risk Management Framework for specific application to the financial services industry.8 These publications are two of six such deliverables that the partnership plans to publish to “provide a foundation for the use of AI in financial services, addressing governance, data practices, transparency, fraud, and digital identity in an integrated way.”9

The Treasury partnership describes the Framework as “an industry‑led, sector‑specific AI risk management framework developed through public‑private collaboration with more than 100 financial institutions and input from U.S. and international agencies, including NIST. Structurally aligned with the NIST AI RMF and expanded with 230 Control Objectives, it helps financial organizations of all sizes manage and govern AI risks while enabling responsible innovation.”10

The Framework consists of four components: (1) an AI adoption stage questionnaire; (2) a risk and control matrix; (3) a user guidebook; and (4) a control objective reference guide. The Treasury partnership also published a website which provides information related to, and at times facilitates participation in, the Framework.

Conclusion

These recent developments indicate that AI is now officially on the radar of U.S. financial authorities, regulators, and private industry groups. Though the FINRA Report, the AI Lexicon, and the Framework are all styled as non-binding guidance, they may nevertheless indicate a trend toward increased scrutiny of AI practices and form standards against which financial services companies could be evaluated.

If your business provides financial products or services and utilizes or plans to onboard any AI technologies, now is the time to begin developing an AI compliance strategy and program that takes into account the above guidance in addition to new and emerging state AI and data protection laws.

The Taft’s Finance and Privacy, Security, and AI and FinTech practice groups have experience helping clients in the financial services industry develop risk-based compliance strategies for AI and other data protection laws, regulations, and standards.

Footnotes:

  1. 2026 FINRA Annual Regulatory Oversight Report at p. 24. ↩︎
  2. Id. ↩︎
  3. All quoted language in bullets from the 2026 FINRA Annual Regulatory Oversight Report at 26. ↩︎
  4. Winning the Race: America’s AI Action Plan (July 2025). ↩︎
  5. U.S. Department of the Treasury, Treasury Releases Two New Resources to Guide AI Use in the Financial Sector. ↩︎
  6. Financial Services Sector Coordinating Council, Financial Sector Artificial Intelligence Executive Oversight Group Deliverables (Feb. 19, 2026). ↩︎
  7. Financial Services Coordinating Council, Artificial Intelligence Executive Oversight Group AI Lexicon (Feb. 2026). ↩︎
  8. Cyber Risk Institute, Financial Services AI Risk Management Framework (Feb. 2026). ↩︎
  9. U.S. Department of the Treasury, Treasury Releases Two New Resources to Guide AI Use in the Financial Sector. ↩︎
  10. Cyber Risk Institute AI Risk Management Framework. ↩︎

AI lawsuits are increasing as businesses use AI-generated logos and brand names without trademark clearance. Learn how the Lanham Act applies and how Taft can help mitigate risk.

Artificial intelligence is transforming modern branding. Companies now use AI to generate product names, logos, taglines, social media campaigns, and even full-scale brand launches in minutes.

The efficiency is undeniable. The legal exposure, however, is often overlooked.

As discussed in recent legal commentary, businesses increasingly face AI lawsuits tied to trademark infringement, false designation of origin, and intellectual property disputes stemming from AI-generated branding.

At Taft, attorneys working at the intersection of intellectual property, emerging technology, and business litigation are seeing firsthand how AI adoption is outpacing risk management.

#AILawsuits #TrademarkLaw #ArtificialIntelligence #IntellectualProperty #LanhamAct #TaftLaw #industriallawyer #insurancelawyer #commericallitigation #privacylaw #datasecuritylaw #whitecollardefense #defenselaw #classactionlawsuits #AIlawsuits #AIdatabreach

Insurance coverage for trademark infringement lawsuits is far narrower than most executives realize.

In this video, Bill Wagner, a partner in Taft’s Indianapolis office, explains what CGL policies may cover, why willfulness allegations destroy coverage, and how insurer-appointed defense counsel can put companies at risk.

Under new regulations effective January 1, 2026, California regulators now expect businesses to conduct an annual “cybersecurity audit” that assesses “how the business’s cybersecurity program protects personal information from unauthorized access, destruction, use, modification, or disclosure; and protects against unauthorized activity resulting in the loss of availability of personal information.”

Now is the time to prepare for these requirements.

As explained below, these requirements are detailed and contemplate a rigorous, professional, independent, evidence-based audit. Audit results must be shared with the California regulator under penalty of perjury.

Applicability & Distinction from Risk Assessments

California cybersecurity audit requirements apply generally to businesses which process the personal information of at least 250,000 consumers or households (50,000 if sensitive personal information), or any business that derives 50% or more of its revenue from “sale” or “sharing” of data. The cybersecurity audit requirements apply to any business which meets the volume and activity thresholds. Businesses should likely consider whether they may meet these requirements given their processing of any California-origin data for any reason (e.g., website visitors, customer data, etc.).

The cybersecurity audit requirements stand separately and distinctly from California risk assessment requirements. Unlike risk assessments, cybersecurity audits generally do not aim to assess specific processing activity separately. Instead, the cybersecurity audit aims to assess the quality of the overall cybersecurity program. The implicit assumption of the regulation therefore seems to be that businesses have such an over-arching cybersecurity program, and that such extend protections to all California resident information. That program will be assessed by the cybersecurity audit.

Timing

The regulation contemplates that larger businesses (gross revenue > $100MM) will be the first to submit a comprehensive cybersecurity audit report in April of 2028, for the period January 1, 2027-January 1, 2028. Eventually, however, all businesses meeting applicability requirements will need to conduct and submit requirements. After the first submission, the expectation is that audit reports will need be submitted annually thereafter in April for the preceding year.

Businesses should strongly consider conducting an advance cybersecurity assessment – a “mock” audit – in 2026. An advance assessment can potentially provide an opportunity to identify and repair certain issues before mandatory audit and submission to the California regulator.

Auditors – Professional, Independent, and Evidence-Based

The cybersecurity audit must be conducted by a “qualified, objective, independent professional, using procedures and standards accepted in the profession of auditing.” Auditors must have both specific cybersecurity knowledge and knowledge of “how to audit a business’s cybersecurity program.” The regulations give auditors real authority to compel the business to provide relevant information. Companies should think carefully about their selection of auditor under these standards.

For companies with a strong internal audit function, internal auditors are permitted. However, crucially, the lead internal auditor must report directly to a member of the business’s executive team who does not have direct responsibility for the business’s cybersecurity program. This likely means that the cybersecurity audit function cannot fall under the information security organization itself or be the responsibility of the CISO.

The regulation contemplates that auditors will independently examine evidence to prepare their findings. Auditors may not base findings “primarily on assertions or attestations by the business’s management.”

Reliance on Other Audits

Given certain detailed audit requirements particular to California law, it is likely that existing audits conducted by a business likely will not suffice for purposes of satisfying California requirements. Businesses may, however, partially utilize and supplement existing audits, assuming adequate scope and that such audits otherwise meet California requirements. Organizations may want to consider conducting a crosswalk or mapping to identify how their existing audit frameworks correspond to California requirements.

Detailed Audit Requirements

California regulations provide a detailed list of issues and controls that must be assessed as part of the audit. This detail defies any sort of usable general summary. For very limited example, the cybersecurity audit must assess:

  • “oversight of service providers, contractors, and third parties to ensure compliance with [detailed California contracting requirement]”
  • “Personal information inventories (e.g., maps and flows identifying where personal information is stored, and how it can be accessed) and the classification and tagging of personal information (e.g., how personal information is tagged and how those tags are used to control the use and disclosure of personal information)”; and
  • “Internal and external vulnerability scans, penetration testing, and vulnerability disclosure and reporting (e.g., bug bounty and ethical hacking programs)”

Among many other detailed requirements. The audit report must detail gaps, weaknesses and remediation plans in areas covered.

Submission Under Penalty of Perjury

Once completed, the cybersecurity audit must ultimately be certified by an executive responsible for the audit and knowledgeable enough to provide accurate information. This executive will need to submit the audit to the California regulatory under penalty of perjury. Submission in this form strongly suggests that the executive may be held personally liable for inaccuracies, perhaps especially if deemed to be intentional falsehoods. Companies should probably anticipate that in the event of any adverse interaction with the regulator, their past audit reports may become a particular point of regulatory scrutiny, and certifying executives may be asked to give testimony.

Legal Support

Experienced counsel can help businesses prepare in at least a few different ways for cybersecurity audits. First, counsel can help assess and confirm applicability requirements. Counsel can help bridge the gap between regulatory text and implementation by working with internal or external auditors who validate audit design and ensure that detailed audit requirements are understood. Once an audit report is prepared in draft form, experienced counsel can advise on the final form of the document that will ultimately need to be submitted to an increasingly active and punitive privacy regulator.   For more information, please do not hesitate to contact a member of Taft’s Privacy, Security, and Artificial Intelligence team.