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Keep an unpublished manuscript out of AI training systems by controlling every place it can be uploaded. Do not put the file into a consumer or external AI service unless its current terms expressly protect confidentiality and provide a no-training or equivalent reuse opt-out. Require written permission before an agent, editor, reviewer, contractor, or publisher uploads it, and put that restriction in your contract.
For text you publish online, reserve your rights in clear language, configure robots.txt, and add machine-readable text-and-data-mining (TDM) reservations where your platform supports them. Those measures create evidence of your instructions and can reduce unwanted collection, but no notice or crawler rule can guarantee that every bot complies or remove copies already collected.
Start by separating the four ways a manuscript can enter AI systems
“Keeping a manuscript out of AI training data” is not one technical switch. The risk and the remedy depend on where the text is held and who controls that location.
| Exposure point | What can happen | Most effective control |
|---|---|---|
| Private, unpublished file | An AI assistant, cloud service, or contractor receives the document and may retain or reuse it under the service terms. | Do not upload it without confidentiality and no-training terms; use an account-level opt-out and written permission. |
| Publisher or editorial workflow | A publisher, editor, publicist, or reviewer sends the manuscript to a consumer-facing AI system for summaries, assessments, copyediting, or marketing. | A contract clause requiring your prior written permission and prohibiting third-party training. |
| Published website or excerpt | Crawlers copy publicly accessible text into indexes, datasets, or model-training pipelines. | Rights reservation, crawler directives, TDM signals, and monitoring. |
| Already copied material | A service or site has retained a copy before you set a restriction. | Document ownership and dates, request removal or a takedown, and obtain legal advice when necessary. |
A website directive cannot protect a file that was previously uploaded to an AI service, and a private-account setting cannot control a publisher’s website. Treat each surface separately.
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Protect an unpublished manuscript before anyone uploads it
1. Classify the file and its permitted uses
Mark the working copy as confidential and identify everyone who may handle it. Include coauthors, translators, sensitivity readers, freelance editors, agents, publicists, and software vendors. Decide which tasks, if any, may use an AI tool. A blanket prohibition is easier to administer than a list of exceptions; if you do allow a task, name the exact service, account, purpose, retention period, and deletion procedure.
Keep a dated register of versions and recipients. A simple record containing the file name, hash or version number, recipient, date sent, permitted purpose, and return or deletion date gives you evidence if a copy later appears elsewhere.
2. Check the AI tool’s current terms before use
Read the service’s terms, privacy policy, enterprise terms, and settings for the specific account and region you will use. Look for four separate commitments:
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- Training and reuse: the provider does not use your prompts, files, or outputs to train or improve models, or gives you a functioning opt-out that covers the uploaded file.
- Retention: you can set or request deletion and understand how long backups, abuse-review copies, and logs remain.
- Control: your organization can administer the setting and obtain a record that it was enabled.
Elsevier’s author guidance tells authors to review each tool’s terms, avoid restricted or sensitive uploads unless confidentiality is assured, and ensure the tool does not train on their materials. It also says authors may not upload previously published or copyrighted content into external, open AI tools. Oxford University Press similarly says authors must not upload work intended for publication into a tool that does not permit opting out of reuse or training.
If the terms are ambiguous, assume the service is unsuitable for an unpublished manuscript. A “do not use my data” sentence in a prompt is not a substitute for a provider-level contractual or account control.
3. Prefer local or contractually governed processing
When an AI-assisted task is genuinely necessary, use a locally run system or an organizational service with written confidentiality, retention, and no-training commitments. Confirm that connected features—such as file search, plug-ins, transcription, analytics, or human review—are covered too. Disable history, model-improvement, and sharing features where the provider offers them, then save a screenshot or export showing the setting and date.
Do not paste a complete manuscript merely because a tool can accept a large context window. Supply the smallest excerpt needed for the task, remove personal information and unpublished plot details where possible, and keep the original file outside the service.
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Put control of publisher and editor uploads in writing
Use a specific permission clause
Verbal assurances do not give you a durable record of what was allowed. Ask your agent, editor, publisher, and any production vendor to confirm in writing whether they will use AI, for which tasks, and under which account and terms. The Authors Guild model language is a useful starting point:
“Publisher shall not upload the Work or any of Author’s personal information to consumer-facing AI systems for purposes such as generating summaries, assessments, or marketing copy without written permission from the author or as otherwise agreed to hereunder; and when such permission is granted, it shall ensure that the manuscript is not used by third-party AI companies for training, such as by opting out of allowing training in user settings.”
Have a publishing-rights lawyer adapt the clause to your agreement, jurisdiction, and bargaining position. Define “AI system” broadly enough to cover chatbots, writing assistants, transcription and translation services, internal model-hosting, and subcontractors. Require the publisher to flow the restriction down to printers, distributors, freelance publicists, and other vendors that receive the manuscript.
Specify what happens when permission is granted
A permission form should identify the work and version, the provider and account, the exact purpose, whether a human reviewer can see the content, the maximum retention period, and the date by which the file must be deleted. Require written confirmation of deletion when the task ends. State that permission for one task does not authorize model training, disclosure, or a later upload for a different purpose.
Ask the workflow questions before delivery
- Will the manuscript be placed in a shared drive, ticketing system, or document-review platform that has its own AI features?
- Are automatic summaries, grammar suggestions, translation, transcription, or marketing generators enabled by default?
- Can subcontractors or platform support staff access the file?
- Which account setting prevents training, and who is responsible for checking it after policy changes?
- How will the publisher notify you of a security incident or an unintended upload?
Request answers in the same email or contract schedule that identifies the manuscript. Keep the response with your version register.
Reserve rights in the book and every online posting
Use a clear copyright-page notice
The Authors Guild recommends a short notice on the cover or copyright page. Its sample wording is:
“NO AI TRAINING: Without in any way limiting the author’s [and publisher’s] exclusive rights under copyright, any use of this publication to ‘train’ generative artificial intelligence (AI) technologies to generate text is expressly prohibited.”
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Agree with your publisher on whether the notice appears in the copyright page, front matter, ebook metadata, audiobook description, or all relevant formats. Retain a dated proof showing the language that was actually published. A notice records your reservation; it does not erase a copy that a system collected before publication or compel every provider in every country to honor it.
Repeat the reservation on web pages
Place a concise rights statement next to an excerpt, serialized chapter, newsletter archive, or downloadable sample. Make the page’s ownership and permitted uses easy for a human or automated system to identify. Do not rely on an image containing the notice; include ordinary HTML text as well.
If a license allows quotation, fan discussion, accessibility conversion, or classroom use, state those permissions separately so readers can distinguish them from the prohibition on model training. Keep a copy of each page and its publication date.
Configure your website against AI crawlers
Use robots.txt deliberately
Create or edit the robots.txt file at the top level of the relevant domain, test that it is reachable, and make sure a content-management-system update will not overwrite it. The Authors Guild recommends rules that block AI bots. Each provider publishes its own crawler user-agent name; copy that exact name from the provider’s current documentation rather than guessing it.
If you want to block all automated crawling of a private path, a broad rule has this form:
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Disallow: /private-manuscript/This example blocks every compliant crawler from that path, including search engines. A site-wide rule would also reduce search visibility, so use a narrow directory or a separate domain when possible. A robots.txt rule is a request, not an access-control mechanism: a crawler can ignore it, and it does not affect material distributed through email, feeds, mirrors, or a file URL that has already been copied.
Add machine-readable TDM and HTML signals
The International Press Telecommunications Council’s 2025 best practices discuss robots.txt, TDMRep, HTML meta tags, and identifying crawler user agents. Use the implementation supported by your content platform to express that automated text-and-data mining and generative-AI training are reserved. Keep the human-readable notice and the machine-readable signal consistent, and test the rendered HTML and headers after deployment.
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These protocols improve clarity and may help a compliant service honor your preference. IPTC explicitly cautions that robots.txt, TDMRep, and HTML meta tags do not guarantee crawler compliance. They also cannot reach data that was collected before the signal was added.
Protect files with access controls
Do not publish an “unlisted” manuscript URL and assume it is private. Require authentication, use expiring links, remove directory listings, and revoke access after an editor’s review. Robots directives are not a replacement for authentication or encryption.
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Search for distinctive text
Set periodic alerts for the title, author name, and several unusual sentences from each edition. Check major search engines, file-sharing pages, review sites, and AI-generated summaries. Keep the URL, screenshot, date, and a copy of the alleged infringement. Do not upload the discovered copy to another AI service while investigating it.
Use a staged response
- Verify the source: compare the text with your dated manuscript and record what was copied.
- Identify the host: note the site operator, hosting provider, platform, and any stated contact or takedown process.
- Send a focused notice: identify the copyrighted work, the infringing location, your ownership, and the action requested. Preserve the message and delivery record.
- Escalate carefully: if the copy is monetized, repeated, or tied to an AI service, consult a qualified copyright or publishing lawyer about jurisdiction-specific remedies.
The Authors Guild recommends alerts for each book and takedown notices when unauthorized copies appear online. Maintain dated copies of contracts, notices, permissions, tool terms, account settings, and takedown requests. This evidence trail often matters more than a generic warning printed after the fact.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What current law and provider policy do—and do not—settle
The U.S. Copyright Office’s AI initiative is still analyzing copyright and AI-training questions. Part 2, released January 29, 2025, addressed the copyrightability of generative-AI outputs; its training-data analysis remains part of the continuing report. That status means an opt-out, contract clause, or rights notice should be treated as a practical risk control, not a universal legal guarantee.
OpenAI’s public journalism statement says it provides publishers with a process to opt out of its tools accessing publisher websites, while also stating the company’s position that training on publicly available internet material is fair use. That is one provider’s position, not a rule that binds every AI company or jurisdiction.
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Rights exceptions, text-and-data-mining laws, contract rules, and privacy protections differ by country. The European Commission opened a consultation on machine-readable rights reservations under the EU AI Act and the GPAI Code of Practice on December 1, 2025. A notice that is effective or customary in one jurisdiction may not produce the same result elsewhere. Before signing a contract or sending a takedown, obtain advice for the countries that matter to your publication and distribution.
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Choose controls by strength, coverage, and evidence
| Control | Strength | Coverage | Evidence to retain |
|---|---|---|---|
| Provider account no-training setting | Voluntary or contractual promise by that provider; subject to its current terms. | Files sent through that account only. | Terms version, account setting, date, and confirmation. |
| Publisher contract clause | Binding written term between you and the contracting party. | Publisher workflow and vendors covered by the clause. | Signed agreement, schedules, permissions, and deletion confirmations. |
| Copyright-page or web notice | Clear public reservation of rights. | Published edition or identified web page. | Dated proof, page URL, and archived copy. |
| robots.txt | Machine-readable request that compliant crawlers may follow. | Paths on the specified website. | File versions, deployment logs, and tests. |
| TDMRep or HTML metadata | Machine-readable signal; compliance is not guaranteed. | Pages or feeds where the signal is implemented. | Rendered HTML, headers, and implementation date. |
| Takedown request | Case-specific enforcement action, not preventive control. | An identified copy or service. | Evidence of ownership, notice, delivery, and response. |
If someone has already uploaded the manuscript
First identify exactly what was sent, to which provider, under which account, and on what date. Save the applicable terms and settings as they appeared then; providers can change them later. Ask the uploader or organization to invoke the provider’s deletion and data-rights process, disable any model-improvement use, and provide written confirmation of the result.
Notify your publisher or agent under the contract, preserve the original file and version history, and avoid creating additional copies in new services. If the manuscript appears in a generated response, public dataset, or unauthorized website, document the output and seek jurisdiction-specific legal advice. A later robots.txt rule cannot retroactively control a private upload.
A repeatable author checklist
- Label the manuscript confidential and maintain a version-and-recipient register.
- Reject external AI uploads unless current terms provide confidentiality, retention controls, and no-training or equivalent opt-out.
- Save proof that the selected account setting was enabled.
- Obtain written permission for every publisher, editor, reviewer, or vendor upload.
- Put the Authors Guild-style restriction, vendor flow-down, deletion, and incident-notification terms in the publishing agreement.
- Place a rights reservation in the copyright page and online postings, then retain dated proofs.
- Use authentication for unpublished files; do not treat unlisted URLs as private.
- Configure narrow robots.txt rules and supported TDM or HTML signals, testing them after site changes.
- Monitor for distinctive text and keep records of takedown requests and responses.
- Re-check provider settings, publisher policies, and applicable law before each new edition or major workflow change.
Frequently Asked Questions
Does writing “no AI training” on a manuscript legally guarantee that no model will use it?
No. The notice records a rights reservation and communicates your instruction, but it cannot control a provider that ignores it or undo collection that occurred earlier. Pair it with contractual restrictions, account controls, website directives, and enforcement.
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That depends on your contract and the publisher’s policy. Ask for written confirmation before delivery and negotiate a clause requiring your permission for consumer-facing AI uploads, with no third-party training when permission is given.
Will robots.txt remove my book from existing AI datasets?
No. robots.txt addresses future crawling of the specified website by compliant bots. It does not delete copies already collected or govern files shared privately.
Should I paste a chapter into an AI editor if I turn off chat history?
Only after confirming the service’s current terms cover uploaded files, prohibit training or provide an effective opt-out, limit retention, and protect confidentiality. Chat-history controls alone may not cover model improvement, abuse review, connected tools, or human access.
Which law decides whether training on my online excerpt is permitted?
There is no single worldwide rule. Copyright exceptions, text-and-data-mining provisions, contracts, and privacy laws vary by jurisdiction, and policy is still developing. Obtain advice for the countries relevant to your publication.
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