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Do not upload sensitive research data to an AI tool until you have confirmed that the specific use is allowed. The answer depends on the dataset’s classification, participant consent, contracts and data-use agreements, institutional policy, applicable law, and the exact AI service and account configuration. Treat every prompt, attachment, output, log, embedding, intermediate file and derived model as part of a data flow that must be approved, limited and monitored.
For NIH controlled-access human genomic data, the rule is especially clear: NIH’s March 28, 2025 notice says that sharing covered data with public generative AI tools through prompts or other user interfaces violates the policy’s non-transferability provision and the Data Use Certification. That warning applies to covered NIH data and its governing agreements; it is not a universal legal rule for every research dataset.
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Start with permission, not the prompt
Before opening an AI assistant, identify what the proposed workflow would expose and who has authority to approve it. A tool that is acceptable for a public abstract may be prohibited for participant-level records, unpublished results or licensed material.
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Write down whether the input contains any of the following:
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- Direct or indirect personal information, including information that could identify research participants when combined with other data.
- Human genomic or other controlled-access data governed by a repository, sponsor, consent form or Data Use Agreement.
- Confidential research, unpublished findings, peer-review material, trade secrets or information supplied under a contract.
- Credentials, security details, proprietary code or information restricted by an institutional policy.
- Only public, already-cleared material.
Classification is not a formality. It determines which services, users, locations and retention periods can be approved. If the classification or authority is unclear, involve the institutional privacy, information-security, research-governance office or data steward before testing a prompt.
Check the authority that governs the data
Read the consent language, protocol, contract, repository terms and data-use agreement that apply to the exact dataset. Confirm whether processing by an external service, transfer to another country, creation of derived data, or use in model development is permitted. A general institutional approval for cloud software does not automatically approve every AI workflow.
Understand what an AI workflow can expose
“The AI did not train on my data” is not a complete security assessment. A service may still process, store or log content while generating a response, and files can pass through integrations or service providers. Map each stage before approval.
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Inventory every data movement
Record where prompts and attachments originate, where they are processed, where outputs are delivered, and where logs, caches, backups and intermediate files are stored. Note the country or region when the service documentation specifies it. The UK Information Commissioner’s Office (ICO) recommends recording data movements and storage; the U.S. Federal Trade Commission (FTC) likewise recommends tracing information flows and who can access them.
Identify people and systems with access
Check whether provider personnel, contractors, subprocessors, administrators, connected applications or other workspace members can access inputs or outputs. Verify the account’s authentication, role controls, export functions, support access and audit logging. Do not infer these details from a product name: consumer, enterprise, application-programming-interface and locally run deployments can have different terms and technical behavior.
Map persistence
Ask how long the service keeps prompts, uploaded files, generated outputs, safety logs, backups and deleted items. Determine whether data can be reused for service improvement or model development under the selected configuration. Obtain the current terms and technical documentation for the exact service, region and account type; no generic “AI” label answers these questions.
A seven-step approval workflow
- Classify the dataset. Label personal, confidential, controlled-access, contractual and public components. Separate mixed datasets where possible.
- Establish authority. Match the proposed use to consent, protocol, repository rules, contracts, institutional policy and applicable law. Document the approving person or committee.
- Select an approved environment. Use the organization’s approved service for that data class. Verify data use, storage, retention, deletion, personnel access, subprocessors and integrations in the exact configuration.
- Minimize the input. Provide only the fields and rows necessary for the task. Prefer an aggregate, summary, synthetic example or small excerpt over a complete dataset.
- Control access. Apply least privilege, strong authentication and role-based permissions. Limit the AI workspace and its exports to people with a legitimate need.
- Set retention and deletion rules. Define how long inputs, outputs, logs, intermediate files and derived artifacts must remain. Delete unnecessary working files and document what the provider’s deletion controls actually remove.
- Review results and derivatives. Check outputs for confidential content, re-identification clues and unsupported inferences. Assess embeddings, fine-tuned models, model parameters, shared prompts and other artifacts before distributing them.
Keep the approval record with the data-management documentation. Reapproval is required when the model, provider, integration, data type, region or intended purpose changes.
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NIH’s NOT-OD-25-081, released March 28, 2025, addresses controlled-access human genomic data covered by the NIH Genomic Data Sharing Policy and Data Use Certification. It states: “Sharing controlled-access data with public generative AI tools (e.g., third party tools) via prompts or other user interfaces is in violation of the provision on Non-Transferability, and by extension, the DUC.”
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That means an approved user cannot paste covered data into a public generative AI service merely because the task appears harmless or because the service offers a privacy setting. The notice also discusses restrictions on models and model parameters developed with controlled-access genomic data. Treat those models and parameters as potential data derivatives and obtain guidance from the relevant NIH program, repository and institutional officials before creating or sharing them.
NIH’s May 30, 2025 request for information describes possible memorization and leakage concerns when generative AI systems retain or share information. It does not establish that every model memorizes every input, nor does it create a general prohibition for unrelated datasets. Apply the terms that govern your own data.
Minimize before you anonymize
Removing information is useful only when the remaining data still meets the research purpose and the resulting risk is understood. Start by asking what the model must see to perform the approved task.
Remove unnecessary fields and rows
- Replace a full table with the specific columns needed for the analysis.
- Use a statistical aggregate or a bounded excerpt instead of participant-level records.
- Strip free-text notes, locations, dates, rare conditions and other details that are not required.
- Keep the original dataset in the approved research environment rather than copying it into a prompt workspace.
Understand pseudonymisation
Replacing names with study IDs can reduce casual exposure, but it does not automatically make the dataset anonymous. The ICO states that pseudonymised information remains personal data when a person is still identifiable, directly or indirectly. Keep the re-identification key separate, restrict access to it and assess linkage risks from rare attributes and external datasets.
Use privacy-enhancing methods deliberately
The ICO identifies perturbation, synthetic data and federated learning as possible techniques. Each changes the threat model and may affect scientific validity. Differential privacy can be difficult to implement meaningfully; a label or a software option is not proof that the chosen privacy budget protects this study. Document the method, parameters, utility trade-offs and attacks it is intended to resist.
Compare workflows by risk, not marketing labels
When choosing between a public service, an institutionally approved hosted service, an API integration or a locally managed system, compare the same questions for each option.
| Decision area | Questions to document |
|---|---|
| Permission | Does institutional policy and the governing consent, contract or data-use agreement permit this exact processing? |
| Data location | Where are prompts, files, outputs, logs, backups and intermediate artifacts processed and stored? |
| Access | Which provider personnel, contractors, subprocessors, administrators and connected applications can access content? |
| Retention and reuse | What are the service’s retention, deletion, backup and data-reuse terms for this account and configuration? |
| Minimisation | Can the research question be answered with less data, less identifiable data, an aggregate or a synthetic example? |
| Derived artifacts | How will outputs, embeddings, fine-tuned models, parameters and shared prompts be governed? |
| Incident response | Who receives alerts, investigates exposure, preserves evidence and reports an incident? |
Do not treat a local model, an encrypted device, an API, or a setting that disables training as an automatic compliance decision. Those features may reduce particular risks, but they do not answer consent, access, retention, derivative-data or institutional-policy questions by themselves.
Limit access and keep an audit trail
The FTC’s Protecting Personal Information: A Guide for Business states: “Scale down access to data. Follow the ‘principle of least privilege.’” Although the guide is general business guidance rather than AI-specific regulation, the principle applies to research AI workflows.
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- Give access only to people who need the data for the approved task.
- Use separate roles for data owners, analysts, administrators and reviewers.
- Require strong authentication and remove accounts promptly when roles change.
- Restrict downloads, exports, sharing links and integrations.
- Log prompt submissions, file transfers, administrative changes and exports where policy permits.
- Review logs for unusual volume, unexpected destinations or attempts to bypass approved tools.
Preserve enough documentation to reconstruct what was sent, when, by whom, under which approval and what was produced. Keep audit records themselves within the applicable security and retention controls.
Set realistic retention and deletion expectations
Define retention for every component: source files, prompts, generated text, images, embeddings, logs, cached copies, backups, temporary conversions and model checkpoints. The correct period depends on the protocol, law, contract, publication obligations and institutional records policy.
Delete unnecessary intermediate files and temporary exports. Before relying on a “delete” button, determine whether it removes active copies only, whether backups persist, and whether provider personnel or subprocessors retain access. Do not promise that every copy has been erased unless the current service terms and technical documentation support that statement.
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An output can reveal sensitive information even when the prompt was minimized. Inspect generated text, code, tables and summaries for participant details, rare combinations, confidential findings, credentials and unsupported inferences before sharing or publishing.
Also govern artifacts created during the workflow. Embeddings, fine-tuned checkpoints, model parameters, retrieval indexes, prompt libraries and evaluation sets may contain or reconstruct information from the source data. NIH specifically treats certain models and parameters developed with controlled-access genomic data as derivatives subject to restrictions.
NIST’s AI security and resilience work highlights confidentiality, integrity and availability risks and notes that existing frameworks do not comprehensively address every AI attack, including model extraction and membership inference. Use that as a reason to reassess the workflow, not as evidence that a particular attack will occur.
Common assumptions that fail
“I disabled training, so the upload is safe.”
That setting may address one form of reuse, but the service can still process, log, store or expose the content through access paths covered by its terms. Permission and data-flow review remain necessary.
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Local processing can change the network and provider-access risks, but the dataset is still governed by consent, contracts, institutional policy and law. Administrators, backups, plugins and shared model artifacts can create additional access paths.
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“The laptop is encrypted.”
Device encryption helps protect a device at rest. It does not decide whether uploading the data is authorized or prevent exposure through the AI service, logs, exports or collaborators.
“We removed names, so it is anonymous.”
Indirect identifiers and linkage can still identify people. Pseudonymised data remains personal data when identification is possible.
“The prompt was only a small excerpt.”
Minimization reduces exposure but does not cure a prohibition in a data-use agreement. A small fragment can also contain a rare combination or a sensitive result.
Use a staged pilot for an approved workflow
When a service is authorized but unfamiliar, start with public or synthetic material that exercises the same functions. Confirm that authentication, logging, export controls, retention settings and integrations behave as documented. Move to a small, approved subset only after the pilot passes review, and record the model version and configuration used.
Test both ordinary and adverse cases. Ask whether an output can reproduce an input, infer a hidden identifier, reveal a rare combination or expose content through a connected plug-in. Have a second reviewer inspect the prompt template and generated result before the workflow is expanded. A pilot does not override a prohibition in consent or a data-use agreement; it simply limits exposure while technical controls are verified.
Prepare for an accidental upload
If restricted data is sent without approval, stop the workflow and preserve the relevant facts: account, time, files, prompt, recipients, model, region and configuration. Do not keep experimenting or attempt to conceal the event. Notify the data steward, privacy or security office and principal investigator according to the institution’s incident plan. Ask the provider what deletion, access-review and preservation steps are available, but do not assume a deletion request removes backups or downstream copies. Rotate credentials if secrets were included, restrict further access and document the decision about notification, containment and follow-up review.
Reassess when the workflow changes
Repeat the assessment when the model version, provider, account tier, region, integration, retention terms, dataset, consent basis or research purpose changes. Review incidents and near misses, rotate credentials exposed in prompts, revoke unnecessary access and notify the responsible institutional office under the applicable response plan.
The ICO’s AI security guidance is under review following the UK Data (Use and Access) Act and may change. FTC guidance is general rather than AI-specific, and NIST provides security context rather than legal advice. Keep current copies of the rules that actually govern your study and have the data steward confirm the final workflow.
A practical pre-upload checklist
- Data classification and governing agreement identified.
- Authorized owner or review body has approved the use.
- Exact service, account, region and integrations documented.
- Processing, storage, personnel access, subprocessors and retention verified.
- Only the minimum necessary data is included.
- Pseudonymisation or other privacy technique assessed for this threat model.
- Least-privilege access, authentication and logging enabled.
- Deletion and backup expectations documented.
- Outputs, embeddings, models and parameters assigned an owner and classification.
- Incident contacts and reassessment triggers recorded.
Frequently Asked Questions
Can I put confidential research data into ChatGPT?
Only if the exact account and workflow have been approved under your institution’s policy, the consent and data-use agreements, applicable law, and the service’s current terms. A consumer account or a setting that disables training is not automatic approval. For NIH controlled-access human genomic data, NIH’s March 28, 2025 notice specifically prohibits sharing covered data with public generative AI tools through prompts or other interfaces.
Is it safe to upload research data to an AI tool?
There is no universal yes or no. First classify the data, confirm authority, map processing and storage, check who can access content, minimize the input, and define retention and deletion. The answer can differ between a public service, an institutionally approved hosted environment, an API integration and a locally managed system.
Can AI tools train on my data?
Possibly, depending on the service, account, configuration and terms. Even when provider training or reuse is disabled, the service may process, log, retain or expose content through personnel, subprocessors, backups or integrations. Verify the exact documentation rather than relying on a product label.
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How do I anonymize research data before using AI?
Start with minimization: remove unnecessary rows, columns, free-text notes, locations, dates and rare attributes. Replacing names with study IDs is pseudonymisation, not necessarily anonymisation; the ICO says pseudonymised information remains personal data when someone can still be identified. Assess linkage risk and keep any re-identification key separate and restricted.
Does a local AI model make sensitive data safe?
Local execution can reduce some network or provider-access risks, but it does not remove consent, contractual, institutional or legal duties. Administrators, backups, plugins, shared model files and derived artifacts can still create access paths. Approve the complete deployment, not only where the model runs.
What should I do if I uploaded restricted data by mistake?
Stop using the workflow, preserve details of what was sent and when, and notify the responsible data steward, privacy or security office and principal investigator under the incident plan. Request available containment or deletion steps from the provider, but do not assume backups or downstream copies are erased. Rotate exposed credentials and document follow-up actions.
Are embeddings, summaries or fine-tuned models covered by the same rules?
They may be. Outputs and derived artifacts can preserve or reveal source information. Govern embeddings, retrieval indexes, prompt libraries, evaluation sets, checkpoints and model parameters under the applicable classification and agreement. NIH specifically describes certain models and parameters developed from controlled-access genomic data as derivatives subject to restrictions.
How often should an approved AI workflow be reviewed?
Reassess whenever the model, provider, account tier, region, integration, retention terms, dataset, consent basis or intended purpose changes. Also review incidents and near misses. AI security guidance evolves, and NIST notes that current frameworks do not comprehensively cover every AI attack.
The Bottom Line
Use AI with sensitive research data only after the exact workflow is authorized. Classify the data, check consent and agreements, map every movement and access path, minimize what is shared, limit retention, govern derived artifacts and reassess when anything changes. NIH controlled-access genomic data cannot be sent to public generative AI tools under the March 28, 2025 notice.
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