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Do not upload sensitive research data to an AI tool until you have confirmed that the proposed use is permitted by the data’s consent conditions, agreements, institutional rules, and applicable law—and that the exact service configuration meets your security requirements. No single setting, local deployment, or de-identification step makes every research dataset safe to use with AI.
Can I put confidential research data into ChatGPT or another AI tool?
It depends on the dataset, the tool and account configuration, and the rules governing the data. Confidentiality alone does not establish permission. Before submitting prompts or files, check participant consent, data-use agreements, contracts, institutional policy, and any legal restrictions. If those rules are unclear, pause and ask your research-governance, privacy, security, or data-stewardship team.
There is a specific, strong warning for NIH-controlled human genomic data: in a notice released March 28, 2025, the National Institutes of Health said that sharing covered controlled-access data with public generative AI tools through prompts or other interfaces violates the non-transferability provision in the Genomic Data Sharing Policy and the Data Use Certification. NIH also describes restrictions on models and model parameters developed using that data. These conditions apply to covered NIH data and its governing terms; they should not be assumed to apply identically to every dataset.
What should you check before using AI with research data?
Use this sequence before trying a prompt, uploading a file, connecting a repository, or using data to fine-tune a model.
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Identify the data and who can approve its use
Classify whether the material contains personal information, confidential or unpublished findings, controlled-access data, trade secrets, or information limited by participant consent or contract. Confirm who has authority to approve the proposed processing. For covered NIH genomic data, consult the applicable policy and Data Use Certification, including NIH’s March 28, 2025 notice.
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Check the exact service and configuration
Use an environment approved for the relevant data class, then review the terms and settings for the actual account or deployment. Establish whether prompts, attachments, outputs, logs, and intermediate files are processed or stored; who can access them, including provider personnel and subprocessors; how long they are retained; whether they may be reused; and what deletion means in practice. Consumer, enterprise, API, and locally run deployments may have different terms. Do not infer that one has the same protections as another.
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Decide whether the task needs the data at all
Determine whether the research purpose can be met with a smaller excerpt, summary, aggregate, or synthetic dataset rather than original records. If data must be used, identify only the fields and detail necessary for the approved task.
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Plan access, records, and retention
Limit access to people with a legitimate need. Document relevant data movements, storage locations, and approved processing steps. Set retention and deletion expectations for inputs, outputs, logs, and intermediate files under the protocol, institutional rules, law, and service terms; remove unnecessary intermediates. Do not assume that deleting a file in an interface deletes every provider-held copy unless the provider’s current terms and technical behavior establish that.
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Consider outputs and derived artifacts
Decide how to handle generated outputs, embeddings, fine-tuned models, model parameters, or shared tools that may be derived from or expose underlying data. NIH’s requirements for derivatives of covered genomic data are one example of why this review matters; other datasets may have different conditions.
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Record the decision and revisit it when things change
Record the approved purpose, data, service configuration, safeguards, and responsible decision-maker. Reassess if the provider, model, integrations, data type, access arrangements, or intended use changes.
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How can you reduce the amount of data you share?
Apply data minimisation to the specific task, not as a blanket promise of anonymisation.
- Remove direct identifiers and sensitive columns that are not needed, where doing so remains valid for the research purpose.
- Use a short, relevant excerpt or an aggregate result instead of pasting a complete dataset.
- Avoid including free-text notes or metadata that could reveal a participant, site, or unpublished result unless they are necessary and approved.
- Keep any key that links a code or pseudonym back to a person separate and protected under the applicable rules.
Pseudonymised information can still be personal data when a person remains identifiable. The UK Information Commissioner’s Office (ICO) makes this distinction in its AI guidance; replacing a name with a code does not, by itself, remove data-protection obligations.
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Not automatically. Whether a transformation reduces risk enough depends on the data, the people who might access it, what other information is available, and the intended use. Removing names may leave combinations of details that identify someone, and pseudonymised data may remain identifiable.
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The ICO identifies techniques such as perturbation, synthetic data, and federated learning as possible privacy-enhancing approaches. They are options to assess, not certifications that a workflow is safe. The ICO also cautions that differential privacy can be difficult to implement meaningfully. Choose a method against a defined threat model and research need, and involve appropriate privacy or security expertise where the risk warrants it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should you compare when choosing an AI workflow?
Evaluate the specific tool and configuration against the same practical questions. The ICO’s guidance is framed in the UK data-protection context; the Federal Trade Commission’s (FTC) guide is general U.S. business security guidance rather than AI-specific policy. Neither substitutes for the rules governing your study or institution.
- Permission: Does institutional policy and each relevant consent, contract, or data-use agreement allow this processing?
- Data movement: Where are prompts, files, outputs, and logs processed and stored, including through integrations and service providers?
- Access: Which users, administrators, provider personnel, or subprocessors may access the content, and what controls apply?
- Retention and reuse: What do the current terms for this exact service configuration say about retention, deletion, and using submitted content?
- Data minimisation: Can the research purpose be met with less data or less identifiable data?
- Derived artifacts and incidents: How are outputs, embeddings, models, or other derivatives handled, and what is the process if data is exposed or mishandled?
The ICO recommends documenting data movements and storage, maintaining audit trails, and considering retention and unnecessary intermediate files. The FTC recommends limiting access according to least privilege, tracing who has or could have access, and retaining sensitive information only as long as needed. These are useful security practices, but they do not establish that a particular AI provider or account tier is approved.
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Can an AI model or its outputs expose research data?
Assess outputs and derived artifacts as part of the workflow, especially if a model, embedding, or tool will be retained, shared, or made available to others. NIH’s May 30, 2025 request for information discussed possible memorization and leakage concerns in generative AI, while its controlled-access genomic data notice sets specific conditions for certain derivatives. These are reasons to evaluate the actual use and applicable terms—not grounds to claim that every model memorizes its inputs or that every output reveals them.
Do not treat disabling training, running a model locally, encrypting a device, or removing names as automatic proof of compliance or safety. Those measures may address particular risks, but the sources cited here do not establish them as universal assurances.
When should you review the decision again?
Review the workflow when its risk conditions change: for example, when the provider or model changes, a new integration is enabled, access expands, a different data class is introduced, or the purpose shifts from internal analysis to sharing or publication. NIST’s AI security overview describes confidentiality, integrity, and availability risks and notes that existing frameworks do not comprehensively address some AI-related attacks, including model extraction and membership inference. Treat approval as specific to the reviewed workflow, rather than as permanent approval for every future use.
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