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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsProtect unpublished research by treating two risks separately: what you upload to an AI service and what automated crawlers can collect from a website. An upload gives a third party possession of the material, so you must check that service’s current retention, access, reuse and training terms before sending anything. A website needs layered controls: a clear rights notice, supported metadata, crawler-specific instructions and, for genuinely sensitive work, authentication or network blocking. A “private” label, copyright notice or robots.txt file is not a universal guarantee and cannot undo collection that already happened.
The practical sequence is to classify the material, apply the strictest institutional and publisher rules, use an approved closed environment where necessary, and document every decision. The controls below reduce exposure while making their technical and legal limits explicit.
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Start with the two exposure paths
Unpublished work can reach an AI system in two fundamentally different ways. The first is deliberate submission: a researcher, reviewer or editor pastes text into a chatbot, uploads a manuscript for summarisation, or sends data through an API. That transaction discloses the material to the service provider, even when the product is advertised as private. The provider’s contract and settings determine what happens next.
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The second path is public availability. A manuscript, preprint, dataset, figure or project page placed on an openly reachable server can be fetched by search crawlers, AI data collectors and other automated clients. A rights statement communicates your position; crawler instructions ask compliant agents not to fetch; server-side controls can actually deny access. These mechanisms have different force and should be combined rather than treated as substitutes.
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IPTC’s guidance for publishers recommends combining human-readable notices, metadata and technical controls, while warning that some crawlers may ignore instructions. Its overview is available at IPTC’s generative-AI opt-out guidance, with the detailed recommendations in the IPTC best-practices PDF.
Classify the material before using any AI tool
Do not begin by asking whether a particular chatbot is safe. Begin by identifying what would be exposed if the tool, an employee or a subcontractor could access the submission. A short classification record helps you apply the right rule to each file.
- Publication status: mark drafts, submitted manuscripts, accepted-but-unpublished articles, confidential reports, lab notebooks, source code and unpublished figures.
- Review status: treat manuscripts and referee reports received for peer review as confidential, even if the authors have posted an earlier version elsewhere.
- Commercial sensitivity: identify proprietary methods, patentable results, trade secrets, sponsor deliverables and information covered by a non-disclosure agreement.
- Personal and regulated data: flag identifiable participant information, health records, location data, student records and any data governed by a consent form or sector regulation.
- Third-party rights: note text, images, datasets, code or contractual material that you are licensed to read but not to upload to another service.
- Governance constraints: record journal, publisher, employer, funder, ethics-board and institutional-security requirements.
If any category is unclear, treat the material as restricted until the owner or your institution confirms otherwise. Removing a name or a few paragraphs does not necessarily remove confidential information: methods, unusual measurements and combinations of facts can identify a project or participant.
Audit the AI service’s live terms and settings
Terms change, and the same provider may offer different protections for consumer, team, enterprise, education and API accounts. Read the terms and the account controls that apply to the exact service and account you will use on the day of submission. Elsevier’s journal policy captures the core issue: “Even if an AI tool is described as ‘private,’ uploading a manuscript still means sharing confidential and unpublished research with a third-party system.”
Check each of the following questions and save the relevant policy version or a dated screenshot in your records:
| Question | What to verify | Why it matters |
|---|---|---|
| Input retention | How long prompts, files, embeddings and logs remain; whether backups have separate retention periods. | Deletion from the visible conversation may not delete copies held for security, billing or abuse review. |
| Training and improvement | Whether inputs or outputs can be used to train, fine-tune, evaluate or improve models; whether an opt-out is automatic or must be enabled. | A service-use permission is different from permission to use your material for model development. |
| Human access | When employees, contractors or reviewers may inspect content and what confidentiality obligations apply. | Human review can create an additional disclosure path. |
| Provider and subprocessors | Which companies host, process or support the service, and where data is transferred. | Your institution or funder may restrict jurisdictions or subcontracting. |
| Ownership and licence | What rights you grant to operate the service and whether the grant is limited to that purpose. | Upload rights should not silently become a broad commercial licence. |
| Deletion and control | How to delete files, revoke access, export records and request erasure, and what exceptions apply. | A documented process is essential if a file is uploaded accidentally. |
| Account and workspace isolation | Whether administrators, shared-workspace members or linked applications can access the material. | A personal account may not provide the isolation expected for institutional work. |
Elsevier advises authors to ensure that a tool receives only the rights needed to provide the service and does not receive a right to train on uploaded material. Read the current Elsevier generative-AI policy for journals when that policy governs your submission. A separate policy covers Elsevier books and commissioned content; it warns authors not to upload restricted or sensitive content unless the terms protect confidentiality and prevent training.
Use a safer workflow for confidential material
Prefer an approved closed environment
When a project contains unpublished, proprietary or personal information, use the environment approved by your institution, employer or sponsor. A closed deployment or a third-party service with contractual privacy guarantees can reduce the chance of secondary use, but only if the configuration actually provides those guarantees. The U.S. Geological Survey describes closed environments and privacy-guaranteed hosting as possible safeguards for unpublished, sensitive and proprietary information in its guidance on safeguarding USGS information with AI.
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Verify the approved service name, account type, region, retention setting and administrator controls. Do not infer that a consumer account has the same contractual protections as an institutionally managed workspace.
Minimise what leaves your systems
Use the smallest excerpt and the least sensitive representation that can accomplish the task. For example, ask for help with a generic outline or a synthetic example before considering a real passage. Remove direct identifiers only when doing so does not defeat the confidentiality requirement, and keep the original file outside the tool. Do not assume that replacing names with initials protects a rare case, a proprietary sequence or a distinctive result.
Separate preparation from transmission
Prepare prompts and redactions in an approved local editor. Confirm the destination, account and sharing settings immediately before pressing upload. Disable public links and shared history where the service offers those controls. Afterward, record the file name or hash, date, account, purpose, policy version and deletion action.
Do not upload material you are not entitled to share
A licence to read a paper, dataset or image usually does not automatically authorise sending it to an unrelated AI provider. Check copyright, data-use agreements, participant consent, employer ownership and sponsor terms. Publisher rules may expressly prohibit sending confidential submissions or copyrighted content to unrestricted tools.
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Peer-review material is confidential by default. The American Physical Society says a referee may use an AI tool only when its terms state that uploaded manuscripts or reports will not be used as training data; consult the APS appropriate-use policy and recheck it because service terms change. Elsevier’s journal policy likewise instructs reviewers and editors not to upload submitted confidential manuscripts or portions of them into AI tools.
Other publishers set related but not identical conditions:
| Organisation or policy | Practical rule described by the policy | Reader action |
|---|---|---|
| Elsevier journals | Check privacy, confidentiality, limited rights and no-training terms; reviewers and editors must not upload confidential submissions. | Use only an approved tool whose current terms meet those requirements. |
| Elsevier books and commissioned content | Do not upload restricted or sensitive content unless confidentiality and training restrictions are clear. | Confirm the contract and the service terms before any upload. |
| American Physical Society journals | Confidential review material may be used only where the terms exclude manuscripts and reports from training data. | Revisit the terms each time the service or account changes. |
| Oxford University Press | Authors are told not to upload work intended for publication to a tool that does not allow opting out of reuse or training. | Use the OUP author AI guidance as a publication-specific check. |
| IOP Publishing | Scraping, extraction and use of publications to develop or improve commercial AI are restricted without express agreement; permissions may impose technical, operational, security and access conditions. | Review the IOP text-and-data-mining policy and any licence terms. |
| Wiley publishing ethics guidance | Publisher ethics expectations apply to responsible handling of manuscripts and research content. | Consult the current Wiley best-practice guidelines alongside your journal’s instructions. |
These examples are policy requirements of particular organisations, not a universal rule for every journal or country. Follow the strictest applicable instruction from the journal, institution, employer, funder and data steward.
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Protect research that must be reachable on the web
If a project must remain public, layer controls according to the sensitivity and the access you need to preserve.
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Publish a clear rights reservation
Place a plain-language notice on the page that hosts the work and, where the format supports it, in the file’s metadata. State who controls the material, what reuse is permitted, and whether automated collection for AI training or commercial text-and-data mining is reserved. Keep the notice consistent with the actual copyright owner, licence and contractual permissions; a notice cannot create rights you do not hold.
IPTC recommends visible notices and rights statements in metadata so that both people and automated systems can discover the reservation. Metadata can be stripped when a file is copied, so treat it as a signal rather than an access barrier.
Give each crawler an explicit instruction
Use robots.txt rules that name the crawler user agents relevant to your site. A generic rule may not cover a provider’s different agents, and a rule on one hostname does not automatically govern a separate subdomain or media host. Identify the current names in each provider’s documentation and test the deployed file.
For example, Anthropic documents a crawler named ClaudeBot and gives this site-wide pattern:
User-agent: ClaudeBot
Disallow: /Its explanation says restricting ClaudeBot signals that future materials should be excluded from its training datasets. The same documentation distinguishes ClaudeBot, which may gather content for model development, from Claude-User, which retrieves pages at a user’s direction. Read the current Anthropic crawler guidance before deployment because names, purposes and syntax can change.
For a narrower restriction, replace the root path with the path that contains the sensitive work, and repeat the rule for every relevant host. Check that your web server serves the file at the exact origin root, returns a successful response, and does not overwrite it during a deployment.
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Add server-side access controls when prevention matters
robots.txt is a voluntary protocol, not authentication. A crawler that ignores it can still request the page. IPTC therefore recommends network-level protection, such as firewall rules or authenticated access, when content must be kept from non-compliant crawlers. Authentication, an allow-list, a private repository or a VPN can deny the request before the document is delivered, although each adds administration and may reduce legitimate access.
Monitor logs for unexpected user agents, request bursts, repeated downloads and access from networks associated with known scraping activity. Rate limits and bot-management controls can reduce load, but they are not proof that a determined actor cannot obtain a file that is publicly downloadable.
Understand what each control can and cannot do
| Control | What it establishes | What it does not establish |
|---|---|---|
| Visible rights notice | Communicates your claimed rights and permitted or reserved uses to human readers. | It does not technically block a download or bind every actor in every jurisdiction. |
| Metadata statement | Attaches a machine-readable or human-readable signal to a supported file or page. | Metadata can be removed, ignored or lost when content is copied. |
robots.txt |
Instructs crawlers that choose to follow the protocol, with rules based on user-agent and path. | It is not authentication, does not stop non-compliant clients and does not erase prior copies. |
| Firewall, authentication or private hosting | Can deny network access before the document is served. | It requires correct configuration and does not control copies already downloaded by an authorised user. |
| Provider opt-out setting | May signal that future collection or use should stop under that provider’s stated process. | It is provider-specific and may not remove material already collected or affect other providers. |
Anthropic’s description of the ClaudeBot restriction is explicitly forward-looking: it says future materials should be excluded from training datasets. It does not promise removal of content previously crawled. The sources reviewed do not establish a universal correction or deletion mechanism for every provider. If exposure has already occurred, contact the specific provider, preserve evidence and seek institutional or legal advice where appropriate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose controls using a threat and access matrix
There is no single effectiveness score for these measures. The right combination depends on what is exposed, who controls the system and whether your goal is to communicate a restriction or physically prevent access.
| Decision factor | Questions to answer | Typical control choice |
|---|---|---|
| Exposure path | Will the material be uploaded to a service, published on a site, or both? | Term review for uploads; rights, crawler and network controls for sites. |
| Sensitivity | Would disclosure harm participants, patent rights, contracts or publication prospects? | Keep highly sensitive material off public networks and use an approved closed environment. |
| Data protection | Does the file contain personal, health, export-controlled or otherwise regulated data? | Apply the relevant privacy and security review before any processing. |
| Control of the service or site | Can you set retention, user access, firewall rules and logs? | Prefer environments where an administrator can enforce and verify settings. |
| Contractual rules | Do a journal, funder, employer or licence impose a stricter prohibition? | Follow the strictest applicable term and obtain written approval for exceptions. |
| Access objectives | Must search engines, collaborators or the public still reach the content? | Use notices and selective crawler rules when access must remain open; authenticate when it must not. |
| Coverage | Which crawler names, hostnames, paths and file stores are involved? | Map every agent and endpoint, then test rules individually. |
| Monitoring burden | Who will review logs, policy changes and failed controls? | Assign an owner and a review interval instead of treating deployment as one-time work. |
| Time horizon | Is the control intended for future access only, or do you need a remedy for past exposure? | Opt-outs address future handling; past exposure requires provider contact and case-specific advice. |
A practical implementation checklist
- Inventory the material. List every draft, dataset, figure, code repository and review file, its owner, sensitivity and publication status.
- Mark prohibited destinations. Record services, accounts, regions and collaborators that your institution or contract does not permit.
- Verify the exact AI account. Read current retention, training, human-access, deletion and subprocessor terms; save the date and account type.
- Obtain approval. Ask the data steward, principal investigator, editor or security office when the classification requires it.
- Minimise the payload. Use synthetic examples, excerpts or local processing where the task allows; never send more than necessary.
- Configure the public site. Add a visible rights statement, supported metadata, and crawler-specific rules on every relevant hostname.
- Block when required. Put sensitive files behind authentication or network controls rather than relying solely on a notice or
robots.txt. - Test from outside your network. Check HTTP responses, authentication, robots syntax, redirects, cached copies and the paths used for PDFs, images and datasets.
- Monitor and review. Watch access logs and provider policy changes; update crawler names and account settings when documentation changes.
- Record the decision. Keep the classification, approval, service terms, settings, robots file, firewall change, access scope and deletion outcome.
This recordkeeping sequence is a governance practice derived from the emphasis that publisher, institutional and technical guidance places on rights, terms and controls. No single global recordkeeping standard covers all researchers.
If you already uploaded or exposed the work
Stop further disclosure
Do not continue using the same conversation, shared link or public endpoint. Revoke links, remove public files where appropriate, rotate exposed credentials and restrict the relevant account or path. Preserve the original material and a copy of the applicable terms before changing settings.
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Document exactly what happened
Record the file or passage, account, recipient, date and time, prompts, attachments, public URL, crawler user agent, server logs and any evidence of downloading. Distinguish confirmed access from a suspected request; this matters for institutional and legal assessment.
Use the provider’s stated process
Request deletion, suppression or clarification through the provider’s published privacy or support channel, but do not assume that a request guarantees removal from backups, logs or model datasets. Ask what future-use opt-out means, whether it covers the specific material and whether previously collected copies are addressed.
Escalate appropriately
Notify your institution, publisher, funder, data-protection officer or research-integrity office when their rules require reporting. For personal data, trade secrets, patent timing or contractual breaches, obtain advice from the responsible privacy, security or legal specialist. The available guidance does not provide one universal remedy for every jurisdiction or provider.
Legal and policy limits
Web scraping and AI training cross borders, contracts and different copyright regimes. The OECD’s 2025 paper, Intellectual property issues in artificial intelligence trained on scraped data, describes scraping as a primary way to obtain training data and examines differing legal and regulatory responses. It does not establish one global answer for every research project.
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What a defensible protection plan looks like
A defensible plan is specific enough that another person can reproduce it. It names the material, the exposure path, the service or hostname, the controlling policy, the setting or rule applied, the date checked and the person responsible for review. It also states what the plan cannot guarantee.
- For an unpublished manuscript used in analysis: keep the source file in an approved closed environment; send only a minimised excerpt to a service whose terms prohibit training and define retention; delete the working copy and record the action.
- For confidential peer review: do not upload the submission or report unless the journal and tool terms expressly allow it without training use; otherwise perform the task locally or decline the AI step.
- For a public project page: publish a rights notice, add supported metadata, list relevant crawler agents in
robots.txt, protect restricted paths with authentication, and monitor logs. - For a result that must remain secret: do not publish it. A public URL, even with a strong notice, gives an authorised visitor a copy and gives non-compliant crawlers an opportunity to ignore your preference.
These choices acknowledge the central trade-off: openness helps collaboration and discovery, while confidentiality requires limiting who can receive the bytes in the first place.
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