Usually, you should not paste scientific data into a public AI chatbot until you have verified permission, classified the data, and confirmed that the specific AI service is approved for that use. A dataset may be de-identified yet still subject to consent limits, an institutional review, a repository data-use agreement, funder rules, or controlled-access conditions. The safe answer depends on the dataset’s source, the task, the people who can access the AI system, its retention and training terms, and the controls your institution requires.
This guide is primarily based on U.S. federal guidance from the National Institutes of Health (NIH), National Science Foundation (NSF), and National Institute of Standards and Technology (NIST). It is not a universal legal checklist. Your IRB or research-compliance office, privacy office, information-security team, data steward, repository, and funder can make the project-specific determination.
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Start with a permission decision, not a chatbot
Before uploading anything, treat the proposed AI workflow as a new form of data sharing. Write down exactly what you want to send, where it came from, why you need the AI, and who could see the material or its derivatives. Do this even when the task appears harmless, such as asking an assistant to summarize a table or improve code comments.
Identify every data element
Inventory more than the main file. Include free-text notes, images, audio, codebooks, linkage keys, filenames, directory names, metadata, derived variables, model checkpoints, prompts, outputs, logs, backups, and any sample used to test the system. A prompt can disclose a participant indirectly when it combines a rare diagnosis, location, date, age, or study identifier.
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Map the data to its restrictions
For each input, record its provenance and the rules attached to it:
- Participant consent language and any limits on future use or sharing.
- IRB determinations, protocol conditions, and institutional policy.
- HIPAA or other applicable privacy obligations.
- Repository access conditions and the data-use agreement (DUA).
- Funder requirements and project-closeout instructions.
- Restrictions inherited from a dataset, codebook, image, or model received from another team.
NIH’s NICHD Office of Data Science and Data Sharing has emphasized that several obligations can apply at the same time. De-identification does not automatically cancel those obligations.
Define the task narrowly
Describe the minimum operation the AI must perform: for example, classify already-approved synthetic records, explain an error message, or draft a statistical-programming template without seeing participant values. A task that can be completed with synthetic examples or summary statistics should not receive row-level data. The less information the system needs, the fewer disclosure and retention decisions you must make.
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| Check | Question to answer | Evidence to keep |
|---|---|---|
| Permission | Do consent, the IRB, law, the repository, and the DUA permit this purpose and this form of disclosure? | Protocol and consent references, DUA section, data-steward decision |
| Classification | Does the material contain personal, health, confidential, proprietary, controlled-access, or otherwise sensitive information? | Institutional data classification and an element-level inventory |
| Environment | Is this exact AI product and account approved for the classification and task? | Security review, approved-service list, contract or institutional authorization |
| Terms | Who can access prompts, uploads, outputs, logs, and backups; how long are they retained; and may they be used for service improvement, training, safety, legal, or operational purposes? | Current product terms, privacy documentation, retention and deletion settings |
| Accountability | Can a qualified person verify the output, document the AI’s role, and meet reporting or record-keeping duties? | Review plan, provenance record, version and date of the AI service |
If any answer is unknown, pause the upload. Ask the data steward or compliance office to resolve the uncertainty rather than relying on a marketing statement such as “not used for training.” Training is only one possible use; storage, human review, logging, backups, and disclosure can matter as well.
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Why a public prompt can become an external disclosure
Public AI services may process an input on infrastructure outside your institution. Depending on the product and account, terms may allow the provider to host, copy, retain, review, use, or disclose prompts and outputs for service delivery, safety, product development, legal compliance, or other operational purposes. Terms differ by provider and can change, so inspect the current terms for the exact service, plan, account, and region.
Do not assume that turning off model training answers every governance question. A no-training setting may leave retention, access by support personnel, security logs, backups, or other permitted uses unresolved. It also does not change a consent restriction or repository DUA.
NSF gives a particularly strict example for its merit-review process. Its December 14, 2023 notice says: “NSF reviewers are prohibited from uploading any content from proposals, review information and related records to non-approved generative AI tools.” NSF also states that proposal information submitted through the open internet violates confidentiality and integrity principles and that information uploaded into tools outside the NSF firewall is considered to enter the public domain for that process. That statement is specific to NSF merit review; it is not a universal legal definition for every AI service or jurisdiction.
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Choose a workflow that matches the data
No deployment model is automatically compliant. Compare the alternatives against the source restrictions, institutional approval, technical controls, and task.
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| Workflow | Typical exposure | Questions that decide whether it is acceptable |
|---|---|---|
| Local or isolated processing | Data remains on equipment or an environment controlled by the research organization, but local accounts, software, administrators, logs, and backups still matter. | Is the software approved? Are the machine, model files, dependencies, updates, and outputs secured? Can the model or logs leave the environment? |
| Institutionally managed secure environment | Processing occurs in an approved research-computing or cloud environment with organizational identity, logging, access control, and contractual terms. | Does the authorization cover this dataset and task? Who administers the system? What are retention, backup, incident-response, and model-derivative rules? |
| Public AI service | Prompts and files cross the institutional boundary and may be retained, reviewed, or used under the service’s terms. | Has the institution expressly approved the exact service and account? Do the terms, settings, region, and deletion process satisfy the DUA, consent, and law? |
NIH’s November 2025 staff toolkit tells NIH personnel not to enter personally identifiable information (PII), protected health information (PHI), or sensitive data into public tools and to use NIH-secure platforms for sensitive information. It lists resources such as CHIRP, an NIH ChatGPT Enterprise license, GENESIS, and STRIDES as examples for NIH staff. Those examples are not endorsements, availability promises, or general recommendations for other institutions; an NIH Institute or Center may impose stricter requirements.
A safe, repeatable AI workflow
- Create a data-and-task record. Name each input, its source, classification, permitted purpose, geographic scope, and retention requirement. Describe the AI operation and the desired output without using real participant data in the description.
- Obtain a documented determination. Ask the IRB or research-compliance office, privacy office, data steward, and information-security team whether the proposed use is allowed. For controlled repositories, consult the repository and the project’s DUA. Keep the decision with the project records.
- Confirm the approved environment. Verify the exact product, account type, tenant, region, model, plug-ins, connectors, administrative access, logging, backups, and deletion behavior. An institutionally managed account is not automatically approved for every dataset.
- Minimize the input. Remove columns, rows, images, identifiers, rare combinations, and metadata that the task does not require. Prefer aggregate values, synthetic examples, masked text, or code that operates inside the approved environment.
- Apply and document de-identification. Record the method, who performed it, what was removed or transformed, and what residual inference risks remain. Preserve linkage keys separately under the approved access rules; do not place them in the prompt.
- Configure access and retention. Use least-privilege roles, strong authentication, approved encryption, network controls, audit logging, and a defined retention schedule. Restrict who can export prompts, outputs, logs, model files, and backups.
- Run a low-risk test. Start with synthetic or public data that exercises the workflow. Check whether the system adds hidden metadata, sends data to connected tools, stores conversation history, or exposes content to other users.
- Record provenance. Save the service and model name, account or tenant, version or date, prompt template, input transformation, output, reviewer, and any material settings. Do not store sensitive prompts in an unapproved note-taking or collaboration service.
- Review every consequential output. A human with subject-matter expertise must check factual accuracy, statistical validity, confidentiality, bias, unsafe inferences, and whether the output accidentally reproduces restricted information. Increase review for clinical, safety, eligibility, authorship, or regulatory decisions.
- Close out deliberately. Delete temporary files and conversations according to the approved schedule, revoke unused access, preserve records that must be retained, and follow the DUA or funder’s instructions for model files and derivatives.
De-identification lowers risk; it does not settle permission
NIH supplemental privacy guidance, issued September 21, 2022, advises researchers and institutions to assess protections proactively, explain future sharing and use clearly in consent processes, justify limitations, and pass those limitations to downstream users. It states: “Researchers and institutions should proactively assess the protections needed for sharing scientific data from participants, including determining whether sharing should be restricted through controlled access.” The guidance is supplemental privacy guidance under the NIH Data Management and Sharing Policy, not a standalone compliance manual; other laws, policies, and repository requirements may apply.
Residual inference risk
Removing names and direct identifiers does not prevent every inference. A model or analyst may combine rare dates, geography, diagnoses, occupations, family relationships, genomic features, or external information to infer identity or sensitive attributes. Assess combinations, not just individual columns. Consider generalization, suppression, perturbation, aggregation, access controls, and a documented threat model appropriate to the scientific utility you need.
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Downstream restrictions must travel with the data
A DUA can prohibit re-identification or recontact, restrict redistribution, require security controls, and define what happens at project end. Put those conditions in the workspace instructions and access approvals for everyone who receives data, outputs, or model artifacts. A de-identified derivative can still be covered by repository terms or institutional rules.
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Special case: NIH controlled-access human genomic data
NIH Notice NOT-OD-25-081, issued March 28, 2025, establishes interim conditions for generative AI involving NIH controlled-access data. It states: “Additionally, NIH considers generative AI models, including model parameters, developed by Approved Users of controlled-access data to constitute Data Derivatives as defined in the DUC provision 14. Definitions.”
Until NIH issues further guidance:
- Use of controlled-access data to develop a generative AI model must be NIH-approved.
- The model and its parameters may be shared only with collaborators who are also Approved Users.
- The model may not be retained after project closeout as instructed by the applicable Data Use Certification (DUC).
These are interim conditions. Check the current notice, the project’s DUC, repository instructions, and any newer NIH guidance before training, fine-tuning, exporting, or retaining a model. Treat checkpoints, weights, embeddings, prompts, evaluation sets, and generated outputs as possible derivatives until the data-access authority says otherwise.
Build security around confidentiality, integrity, and availability
NIST frames AI security around the confidentiality, integrity, and availability of systems and of their training and output data, together with the security of the underlying software and hardware. Use your organization’s established information-security program as the baseline and add controls for the AI workflow. NIST also describes this as an active, rapidly changing area; existing guidance does not comprehensively address every AI-specific attack class.
Confidentiality controls
- Use approved identity, role-based access, multi-factor authentication, and separation of duties.
- Encrypt data in transit and at rest, including temporary files, logs, backups, and model artifacts.
- Block unapproved browser extensions, plug-ins, connectors, and external storage.
- Log access and exports without placing sensitive content into broadly visible monitoring systems.
- Set retention and deletion rules for prompts, outputs, caches, and backups, then test that deletion works.
Integrity controls
- Pin and record model, code, dependency, and prompt-template versions.
- Verify downloaded models and packages; scan for malicious or altered components.
- Separate development, testing, and production data.
- Check outputs for fabricated citations, altered values, prompt injection, data poisoning, and unauthorized transformations.
- Require human approval before an output changes a dataset, analysis, publication, clinical action, or regulatory submission.
Availability controls
- Maintain approved backups and a recovery plan that does not create an unauthorized duplicate.
- Document what happens if the model, vendor, network, or account becomes unavailable.
- Keep a non-AI method for critical analyses and participant-safety operations.
Peer review is a separate, stricter use case
Do not treat a general permission to use AI for research administration as permission to use it for confidential peer review. NIH prohibits its scientific peer reviewers from using generative AI to analyze or formulate critiques for grant applications and research-and-development contract proposals. NSF prohibits reviewers from uploading proposal content, review information, or related records to non-approved generative AI tools.
NSF separately encourages proposers to describe whether and how they used generative AI to develop proposals, while the proposer remains responsible for the accuracy and authenticity of the submission. Follow the current agency notice and the instructions attached to the specific review or proposal process.
If sensitive data was already uploaded
Act quickly but do not attempt to hide the event. Stop further uploads, preserve the relevant account and audit information, and contact your institution’s information-security, privacy, research-compliance, and data-steward teams. Ask the service about deletion, retention, human access, backups, connected applications, and whether the content was used to create derivatives. Notify the repository or funder when the DUA or incident plan requires it. Your institution should determine whether the event is a reportable disclosure and what participant or collaborator notifications are necessary.
Who should approve the workflow?
Responsibility is shared, but approval should be explicit:
- Principal investigator: defines the scientific purpose, necessity, and acceptable output.
- Data steward or repository authority: interprets the DUA, access category, and downstream restrictions.
- IRB or research-compliance office: checks consent, protocol, participant-protection, and applicable regulatory questions.
- Privacy office: evaluates personal and health information, disclosure, and rights implications.
- Information-security team: assesses architecture, identity, encryption, logging, vendors, incident response, and residual technical risk.
- Human reviewer: verifies outputs and records the AI’s role in the analysis or decision.
Keep the approval, data inventory, de-identification record, service terms, configuration, prompts, outputs, review notes, and closeout action in a controlled project record. Reassess when the model, account, task, dataset, collaborators, or service terms change.
Scope and limits of this guidance
The cited federal materials are U.S.-focused. They do not resolve obligations under non-U.S. law, every institution’s policy, every funder’s terms, or any particular commercial AI service. Rules may also be stricter for children’s data, identifiable health information, genomic data, export-controlled work, security-sensitive research, or regulated clinical activity.
The practical rule is therefore conditional: use AI only after the data owner and institution have approved the purpose, environment, controls, and retention plan. When that approval is unavailable, use synthetic or public data, keep processing inside an authorized environment, or perform the task without uploading the protected material.
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