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If you suspect an AI company used your writing, art, music, photographs or video without permission, do not begin with a takedown demand or a lawsuit threat. First preserve the original evidence, confirm who owns the relevant rights, identify whether the issue is training, live retrieval, a generated output or a copy hosted online, and check the provider’s disclosures and complaint process. The legal answer depends on your country and the facts; no opt-out, watermark or notice guarantees that a trained model will forget your work.
Start with an evidence file
A strong record is more useful than a broad allegation. Create a dated folder and keep the original files unchanged. Record what you know, what you infer and what remains unconfirmed.
Confirm ownership and permissions
- Identify the work, its creation date and every relevant publication date.
- Check contracts, assignments, employment terms, commissioned-work agreements, collective-management arrangements and licenses. A platform license or an exclusive assignment may affect who can complain or negotiate.
- List the rights at issue: reproduction, adaptation, distribution, public display or performance, and any rights recognized under local law.
- Note the country or countries connected to the creator, rights owner, provider, audience and alleged use. Copyright exceptions and remedies are territorial.
Preserve technical and documentary proof
- Keep source files, layered project files, export histories, drafts and registration records.
- Save the original URL, publication timestamp and a local copy of the page where the work appeared. Take screenshots that include the address bar and date; save provider statements, policy pages and model or product names.
- For a suspected output, preserve the exact prompt, settings, account, date, model/version, output file and any conversation or API response. Do not edit the output before archiving it.
- Keep correspondence and delivery receipts. Hashes or a trusted timestamp can help show that a file existed in a particular form at a particular time.
Evidence that a model can produce a similar-looking result is not, by itself, proof that a particular work was in training. Treat an inference as an inference and describe the basis for it.
Separate the activity you are challenging
Different technical events create different legal questions and remedies. Write a one-sentence description of the suspected event before contacting anyone.
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Collection or dataset ingestion
A crawler may have copied a page or an image into a dataset. The relevant questions include the law where copying occurred, the terms governing access, whether an exception applies and whether the copy was retained or distributed.
Model training or fine-tuning
Training can involve temporary or permanent copies and may occur in several countries. A provider’s statement that it uses publicly available material does not establish that every item was licensed or that the use is unlawful. Ask which model and update were involved, rather than assuming every product shares one dataset.
Live retrieval or search augmentation
A system can fetch material from a live website when answering a prompt. That is different from having the material embedded in model parameters. Site terms, access controls, licensing and the provider’s retrieval policy may matter.
A generated output that resembles or reproduces your work
Record the output and the comparison points: identical text, distinctive composition, characters, sequence, melody, framing or other expressive details. Similarity alone does not identify the source or settle an infringement claim; expert and factual analysis may be needed.
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A copy hosted by an online service
If a user uploads your photograph, text or video to a platform, the immediate issue may be a specific hosted copy. A platform complaint or a statutory notice can target that copy even when the separate training question remains unresolved.
What the law says depends on where the dispute is
United States: fair use is a fact-specific test
The U.S. Copyright Office describes four fair-use factors: the purpose and character of the use, the nature of the copyrighted work, the amount and substantiality used, and the effect on the potential market. None automatically decides an AI-training dispute. The Office’s Fair Use FAQ explains the factors, but only a court can resolve their application to a particular record.
The Office’s Part 3 report, Copyright and Artificial Intelligence: Generative AI Training, was released in a prepublication version on May 9, 2025. It analyzes training, licensing and liability questions; it is not a judgment in any creator’s case. The study page identifies the report’s status at copyright.gov/policy/artificial-intelligence/, and the report is available at this PDF. More than 10,000 comments were submitted by the December 2023 deadline. That number measures public submissions, not the number of infringements or creators’ losses.
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European Union: reservations and transparency have defined conditions
EU AI Act Recital 105 explains that copyright-protected content generally requires authorization unless a copyright exception or limitation applies. It describes the text-and-data-mining exceptions in Directive (EU) 2019/790 and says rights holders may reserve their rights in an appropriate manner, subject to the applicable conditions. Where a reservation is expressly made in the required way, a provider of a general-purpose AI model (GPAI) needs authorization to mine the reserved works. This is an EU framework, not a worldwide opt-out rule. Read the recital at the European Commission AI Act Service Desk.
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The European Commission also requires GPAI providers placing models on the EU market to publish a summary of training content. The mandatory-template FAQ, updated March 26, 2026, says summaries should identify general data types, dataset sources and information about web-scraped material, including crawler names, collection periods, content descriptions and a list of the top 10% of scraped domains. For small and medium-sized enterprises, the threshold is the top 5% or 1,000 domains, whichever is lower. These are source and domain disclosures, not a work-by-work inventory. Summaries should be updated at six-month intervals or sooner after a materially significant further-training update. Consult the Commission FAQ and template guidance for the named model, version and update date.
The same FAQ says the summary obligation applies from August 2, 2025. Providers whose models were placed on the EU market before that date should make corresponding summaries available by August 2, 2027. Check the Commission page for later amendments before relying on that timetable.
Check what the provider has disclosed
- Find the exact model or service. Capture its name, version, release or update date and the provider’s legal entity.
- Read the copyright and complaint policy. Look for a designated contact, required evidence, response times and rules for repeat complaints.
- Read the training-content summary. In the EU, match the summary’s source categories, crawler information and domain list to your publication history. Absence from a summary is not proof that your work was not used, because the disclosure is not itemized for every work.
- Check crawler and dataset notices. Save the wording and date. A robots directive, metadata field or site term may have legal significance in some settings, but it does not automatically bind every provider or work retroactively.
- Preserve the page. Policies change. Save a PDF or screenshot and record when you accessed it.
Opting out and reserving rights: useful records, no guaranteed erasure
Where the applicable law recognizes a reservation, use the method that law and the provider’s process accept, and keep proof of what was served and when. State the covered domains or works precisely. An opt-out made after a model was trained may affect future collection without changing existing parameters or copies.
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No source establishes a universal mechanism that removes a work from every trained model. A watermark can help identify provenance, but it does not prove unauthorized training and can be stripped or lost. Metadata can be ignored or removed. Treat these measures as evidence and forward-looking controls, not as a promise that a model will forget.
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Contact the provider with a precise request
A first contact should be factual and narrow. Identify the rights holder, the work, the relevant URL or file, the suspected activity, the model/version and the evidence supporting your concern. Say what you want: a disclosure, confirmation of whether the work was used, a correction to a summary, a prospective reservation, a license discussion, removal of a hosted copy or another specific remedy. Ask the provider to identify the correct route if your issue is outside its standard form.
Avoid stating that infringement is certain when the evidence only shows similarity or a possible dataset match. Keep a copy of the submission and delivery confirmation. If the provider offers a license or settlement, obtain advice on scope, exclusivity, territory, duration, training rights, output rights, attribution, audit access and payment before signing.
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In the United States, Section 512 notice-and-takedown is aimed at specific material hosted on a covered online service. The U.S. Copyright Office’s Section 512 resources describe the required elements:
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- Identification of the copyrighted work, or a representative list of works.
- Identification and location of the allegedly infringing material so the service can find it.
- Your contact information.
- A statement of good-faith belief that the use is not authorized by the owner, an agent or law.
- A statement that the information is accurate and, under penalty of perjury, that you are authorized to act.
- Your physical or electronic signature.
A notice is a claim routed through a statutory service-provider process; it is not a court ruling that training infringed. It does not erase copies already used in training or retrain a model. False material assertions can have consequences, so identify only material you can specifically locate and substantiate.
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Generated outputs and your own copyright
If the complaint concerns an output rather than training, analyze the output separately. The Copyright Office’s January 29, 2025 announcement explains that an output can be protected where a human author determined sufficient expressive elements; prompting alone is insufficient, while AI assistance or AI-generated material inside a larger human-authored work does not automatically bar copyright. The announcement is at copyright.gov/newsnet/2025/1060.html.
This distinction can affect both sides of a dispute. Your original photograph or illustration may be protected even if a particular generated output is not. Conversely, a user’s output may contain protectable human-authored selection or arrangement. Preserve the human contributions, drafts and editing history that show how the work was made.
Choose a response that matches your goal
| Goal | Best first step | Main uncertainty | Likely burden |
|---|---|---|---|
| Find out whether a work was used | Review the provider’s model disclosure, training summary and copyright contact; send a documented inquiry. | Summaries may identify domains or categories without naming individual works. | Collecting a reliable work-to-model connection. |
| Prevent future mining where permitted | Serve a legally recognized rights reservation for the relevant site or works and retain proof. | Conditions differ by jurisdiction, and a reservation may not be retroactive. | Correct wording, scope and delivery records. |
| Discuss payment or permission | Send a license proposal that defines works, models, territory, term, uses and compensation. | The provider may deny use or decline to negotiate. | Valuation and contract review. |
| Remove a particular hosted copy | Use the platform’s complaint route or a compliant U.S. notice to the designated agent. | Exceptions, counter-notices and platform eligibility may apply. | Locating the exact copy and proving ownership. |
| Seek damages or an injunction | Consult qualified counsel in the relevant jurisdiction before making a demand or filing. | Liability, defenses, evidence and remedies are fact-specific. | Cost, time, discovery and litigation risk. |
When to get legal advice
Obtain advice before sending a high-stakes demand, accepting a settlement, asserting a large catalog-wide claim or contacting multiple parties. A lawyer or creator-rights clinic can assess jurisdiction, ownership, limitation periods, contract terms, privacy issues and the appropriate defendant or service.
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Ask counsel to separate claims about collection, training, retrieval, output similarity and hosted copies. They may require different defendants, evidence and requested relief. Keep communications accurate and avoid destroying or altering originals while the issue is being assessed.
A practical decision checklist
- Have you identified the work, rights owner, contracts and relevant territories?
- Have you preserved originals, dates, URLs, screenshots, policies, model details, prompts and outputs?
- Can you describe the suspected event as collection, training, retrieval, output reproduction or a hosted copy?
- Have you checked the provider’s complaint route and the applicable training-content summary?
- If relying on a reservation, did you use the legally appropriate method and keep delivery proof?
- Is your requested remedy specific: information, correction, reservation, license, hosted-copy removal or legal relief?
- Before a lawsuit, have you checked registration and obtained advice under the law that governs the claim?
The U.S. Register of Copyrights, Shira Perlmutter, summarized the underlying principle this way: “Where that creativity is expressed through the use of AI systems, it continues to enjoy protection.” Protection of your work does not make every AI use unlawful, but careful evidence, jurisdiction-aware analysis and a remedy matched to the actual activity give you the strongest practical response.
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