The legal risks of using ChatGPT for marketing content

The loudest worry around ChatGPT legal risks for marketing content is usually copyright. That is understandable, because ownership sounds like the obvious legal question when a machine helps write an ad, a landing page, or a campaign concept. But in the day-to-day publishing workflow, copyright is often not the doorway most likely to hurt the team first.

The more immediate exposure is usually closer to the publish button: a factual claim ChatGPT invented, a sponsored or AI-assisted post that lacks the right disclosure, a prompt containing customer or company information, or a generated slogan that lands too close to someone else’s trademark. Those are not abstract AI-policy problems. They are ordinary marketing tasks with ordinary legal consequences.

That distinction matters because “ChatGPT wrote it” does not move responsibility away from the company that publishes the content. In practice, the legal system and regulators care much more about who distributed the claim, who benefited from it, and who had the chance to review it before it went live.

The first risk is publishing a false factual claim

Defamation is where AI hallucination stops being a funny screenshot and starts becoming a demand letter. Marketing teams use ChatGPT to summarize competitors, draft comparison pages, generate sales enablement copy, write executive social posts, and turn messy notes into confident prose. Those are exactly the places where a fabricated “fact” can become a published accusation.

The examples already exist. ChatGPT fabricated a nonexistent Washington Post article that falsely accused a law professor of sexual harassment. In another dispute, Wolf River Electric sought more than $100 million in damages after Google’s AI Overview allegedly said the company faced a state attorney general lawsuit, which the company said led to customer cancellations. Quinn Emanuel’s discussion of these disputes treats them as early signals of a familiar legal problem in a new distribution channel: false statements can still injure reputation, even when software produced the wording.[1]

For marketers, the uncomfortable part is how normal the workflow looks. Nobody needs to ask ChatGPT to “defame a competitor.” A content manager might ask for a comparison between two vendors, a product marketer might request “common complaints about Company X,” or a social media manager might prompt for a punchier LinkedIn post based on an article they barely read. If the output includes an invented lawsuit, safety incident, executive scandal, product defect, or customer loss, the risk is attached to publication, not to the prompt window.

The Walters v. OpenAI result is sometimes read too broadly. In May 2025, a Georgia state court granted summary judgment for OpenAI in a defamation case involving ChatGPT output, but the dismissal turned on narrow facts: the journalist had the actual complaint, knew ChatGPT could fabricate, and did not publish the false output to a general audience in the way a media or marketing publisher might. It was also a state trial court decision with limited precedential force.[1]

That is a thin reed for a brand to lean on. A company publishing a comparison page, category guide, ad, email sequence, or founder post cannot assume the same procedural facts will be available. The safer operating rule is blunt: if the sentence makes a factual claim about a person, company, product, customer result, lawsuit, safety issue, financial condition, or professional conduct, it needs verification outside ChatGPT before publication.

Claims that need human verification

• Competitive claims: “Brand A has been sued,” “Brand B has lower uptime,” or “Brand C is losing customers.”
• Performance claims: “reduces churn,” “doubles conversion,” “saves 10 hours per week,” or “improves retention.”
• Customer claims: named case-study results, quoted praise, industry adoption, or implied endorsement.
• People claims: allegations about executives, employees, creators, reviewers, analysts, or public figures.
• Regulatory or legal claims: lawsuits, investigations, certifications, compliance status, or enforcement history.

The review step cannot be “does this sound plausible?” Plausible is exactly the problem. The reviewer needs to ask where the claim came from, whether the source supports the exact wording, and whether the content turns a narrow fact into a broader accusation.

FTC risk is not just about saying a post used AI

Disclosure is the other area where marketing teams can mistake generated copy for publishable copy. The risk is not limited to whether a caption says “made with AI.” The FTC problem is broader: a consumer-facing message can be deceptive if it hides material information, misstates how something was created, exaggerates a product claim, or fails to disclose a material connection.

HumanAds describes the current FTC posture as a “double-disclosure” framework: marketers should disclose both the AI tool’s role when that fact is material and any material connection such as sponsorship, payment, affiliate compensation, or free product. The same guide states that the FTC’s civil penalty can reach $53,088 per violation, and that each non-compliant piece of content can count separately.[2]

That should not be overstated as a brand-new standalone AI disclosure statute. The legal basis remains the FTC Act’s Section 5 prohibition on unfair or deceptive acts or practices, along with the endorsement rules and existing truth-in-advertising principles. The AI part changes the workflow, the scale, and the kinds of omissions that can occur; it does not create a magic exemption from rules that already applied to ads, testimonials, influencer content, and product claims.

The enforcement direction is no longer theoretical. HumanAds reports that the FTC brought its first AI-generated advertising enforcement action in late 2025 and established a dedicated AI enforcement unit in January 2026.[2] All About Advertising Law’s analysis of a 2026 FTC settlement likewise highlights the risk of deceptive AI marketing claims, especially where companies describe AI capabilities in ways that consumers may reasonably understand as objective product promises.[3]

For a content team, the awkward cases are usually not the obvious ones. A paid creator uses ChatGPT to draft a review and forgets the sponsorship disclosure. A brand publishes AI-generated customer quotes that read like real testimonials. A product page says the software “uses AI to guarantee” an outcome that the company cannot substantiate. A campaign uses a synthetic expert or avatar in a way that implies real professional endorsement. Each one can look like a copy-editing issue until someone asks whether the audience was misled.

The practical review is not complicated, but it has to happen before distribution. Someone needs to check whether the audience would care that the content was AI-generated, whether any commercial relationship is visible enough, and whether every product claim can survive the same substantiation review the team would apply to human-written ad copy.

The prompt box is not a private notebook

The data-leak risk is quieter because it often happens before anything is published. It starts when someone treats ChatGPT like a scratchpad: “summarize this customer interview,” “rewrite this contract paragraph in plain English,” “turn these support tickets into campaign themes,” “analyze this spreadsheet,” or “make this internal strategy memo executive-friendly.”

Cyberhaven tracked activity across 1.6 million workers and found that 11% of the data pasted into ChatGPT was confidential. The company reported that the average company leaked sensitive data hundreds of times per week, including customer personally identifiable information, internal strategy, and trade secrets.[4]

Those figures come from a published snapshot of worker behavior, not a complete census of every current AI workflow. They may also undercount today’s exposure if employee usage has continued to rise. Even with that caution, the finding fits what many content operations teams already see: the risky prompt is often written by someone trying to move faster, not someone trying to break policy.

Marketing has several leak-prone inputs. Customer interviews contain names, roles, internal metrics, unapproved quotes, and implementation details. Sales calls contain objections, pricing sensitivity, and competitor references. Product launch plans contain roadmaps. Partner briefs contain confidential positioning. Support exports contain user complaints and sometimes personal data. None of that becomes safer because the immediate output is a blog outline.

The rule should be written for tired people, not ideal people. Do not paste customer PII, unreleased financials, source code, contracts, private roadmap details, raw sales-call transcripts, proprietary research, or confidential partner material into a public AI tool unless the company has approved that tool, account type, data setting, and use case. If the team needs AI help, redact first or use an approved environment built for that data.

Generated names, slogans, and logos still need trademark clearance

Trademark risk shows up when ChatGPT moves from drafting sentences to naming things. Product names, campaign lines, event names, slogans, logos, and visual identity concepts can collide with existing marks. The model does not need to intend infringement for the company using the output to create a problem.

AALRR’s discussion of trademarks in the age of AI frames the issue as structural: generative tools can produce logos, slogans, and brand names that inadvertently infringe or dilute famous marks, and the absence of human intent does not eliminate liability.[5]

This is especially easy to miss because AI-generated branding often arrives with confidence. A prompt asks for “premium SaaS product names” or “taglines like the best enterprise cybersecurity brands,” and the output feels polished enough to drop into a deck. The danger is that polish can mask proximity. A name can be too similar in sound, meaning, category, or commercial impression. A slogan can echo a well-known campaign. A logo concept can lean on the same visual cues that make another mark recognizable.

The workflow answer is simple: treat AI-generated brand assets as candidates, not cleared assets. Run the same trademark search and legal review the team would run for human-generated names and slogans. For competitive content, also check whether brand names, logos, and comparison references are being used accurately and only as needed.

The publisher owns the risk, even when the tool wrote the first draft

The shared thread across these risks is publisher responsibility. OpenAI’s user-responsibility framing, as summarized by BFV Law, places responsibility on users to comply with applicable law when using ChatGPT output.[6] That contract point lines up with the broader practical reality: regulators, competitors, customers, and plaintiffs will usually aim at the company that published, benefited from, or distributed the content.

That matters more than debates about whether a prompt engineer, freelancer, employee, or model “caused” the problem. If a brand’s landing page contains an unsubstantiated performance claim, the brand has to answer for the claim. If a sponsored post hides the sponsorship, the brand and creator may both have exposure. If a comparison page invents a competitor scandal, the fact that the wording came from ChatGPT does not make the competitor’s reputational injury imaginary.

Open questions remain. Some cases are pending, including disputes that could further shape how courts handle AI-generated defamation and publisher liability. Different facts will matter: who prompted the system, who saw the output, whether it was published, what the audience understood, and whether the company had reason to know the claim might be false. That uncertainty is not a reason to stop using ChatGPT. It is a reason to stop treating AI-assisted copy as legally lighter than human copy.

Copyright is real, but it is usually the wrong lead risk

Copyright still belongs in the conversation. The important correction is that it is not always the most immediate enforcement threat for a marketing team using ChatGPT to draft ordinary content.

The U.S. Copyright Office’s March 2025 position, as summarized in the HumanAds guide, is that fully AI-generated content does not receive copyright protection.[2] The business consequence is easy to underestimate: if a team publishes a fully AI-generated asset, a competitor may be able to copy it without infringing the company’s copyright. That is an ownership and defensibility problem, not the same thing as saying every ChatGPT-assisted post creates an immediate lawsuit risk for the marketer who publishes it.

Human contribution matters. A campaign that uses ChatGPT for rough options, then relies on human selection, editing, arrangement, original examples, brand judgment, and source-based claims is a different asset from a prompt-and-publish output. The copyright analysis can become fact-specific, and teams should not pretend the line is cleaner than it is. But from a workflow perspective, the first controls should still sit where the enforceable risk is closest: claim review, disclosure review, input controls, and trademark screening.

Where the review actually belongs

A usable ChatGPT policy for marketing does not need to slow every draft to a crawl. It needs to slow the specific points where the company becomes exposed.

• At the claim level: verify factual statements about people, competitors, customers, product performance, legal status, certifications, and market position against reliable sources.
• Before promotional publication: check whether AI involvement, sponsorship, affiliate compensation, or another material connection needs disclosure.
• Before prompting: remove confidential, personal, customer, financial, contractual, roadmap, and trade-secret information unless the approved tool and use case allow it.
• Before adopting brand assets: clear AI-generated names, slogans, logos, and campaign identifiers through trademark review.
• Before relying on ownership: decide whether the asset has enough human authorship and originality to be protectable, especially for major campaigns.

ChatGPT can still be useful for outlines, variants, briefs, rewrites, first drafts, and creative exploration. The mistake is letting that usefulness blur the point at which a draft becomes a public claim, an advertisement, a data transfer, or a brand asset. That is where the review belongs.

References

1. Defamation in the AI Era, Quinn Emanuel
2. FTC AI-Generated Content Disclosure, HumanAds
3. FTC Settlement Highlights Risks of Deceptive AI Marketing Claims, All About Advertising Law, June 2026
4. 4.2% of Workers Have Pasted Company Data into ChatGPT, Cyberhaven
5. Trademarks in the Age of AI: The Emerging Legal Battlefield for Brand Owners and Users of Generative AI, AALRR
6. Using ChatGPT for Business? Beware of ChatGPT Risks & Legal Landmines, BFV Law