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Adopt an AI tool only after you can explain the exact task, the data it will see, the controls protecting that data, how a qualified person will verify its output, and which institutional, funder, publication, privacy, and safety rules apply. A tool is not simply “safe” or “unsafe” in the abstract: acceptability depends on the use, data class, jurisdiction, provider terms, and consequences of an error.
1. Define the research task before choosing a model
Start with a written use case, not a brand or model name. State whether the proposed task is literature triage, coding assistance, transcription, image analysis, drafting, data cleaning, or something else. Name the users, the decisions the output could influence, and the benefit you expect over the lab’s current workflow.
Write an acceptance test
Define what acceptable performance means before a pilot. For literature work, that might mean finding relevant papers without inventing citations. For code, it might mean producing an understandable draft that passes an independent test suite. For image analysis, it might mean a qualified researcher can reproduce the classification and identify false positives that matter scientifically.
- List the most damaging plausible failures, not just average errors.
- Decide which outputs require line-by-line or case-by-case review.
- Assign a named researcher who can verify the result independently.
- Specify what prompts, model version, input files, edits, checks, and exceptions must be recorded.
UNESCO’s human-centered approach and NIH integrity guidance both point toward human validation rather than treating generated output as an authoritative result. If no suitably qualified person can check an output, do not use the tool for that decision.
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2. Map every input and output as a potential data disclosure
A prompt is a data transfer. So are an uploaded spreadsheet, an image, a pasted error log, attached metadata, and a generated output that contains a transformed version of the original information. Inventory the full path before anyone signs in.
Classify the material
| Material | Questions to answer | Typical decision gate |
|---|---|---|
| Participant identifiers, clinical records, or protected health information | Does the consent, protocol, privacy review, and applicable health-information rule permit disclosure to this service? | Obtain institutional privacy and IRB guidance before sending any identifiable or linkable data. |
| Controlled-access human genomic data | Does the Data Use Certification authorize transfer to this provider and this processing purpose? | Do not place it in a public AI interface unless the authorized data-use terms explicitly allow it. |
| Unpublished findings, manuscripts, figures, or grant material | Could retention, training, logging, or a security incident expose confidential work or affect intellectual-property rights? | Use only a service and contract approved for that confidentiality level, or remove the sensitive content. |
| Peer-review material or collaborator and sponsor information | Do confidentiality agreements or review-system rules prohibit external processing? | Keep the material out unless the responsible institution and governing system authorize the use. |
| Derived data and model outputs | Could the output reveal, reproduce, or enable reconstruction of restricted inputs? | Apply the same or a stricter handling rule than for the source data until an authorized assessment says otherwise. |
NIH warns that sending research inputs to an external AI provider can create an unauthorized disclosure. For controlled-access genomic data covered by the NIH Genomic Data Sharing Policy and Data Use Certification, the non-transferability provision means a public generative-AI prompt can violate the authorized-use conditions. NIH also treats models developed by approved users as data derivatives and limits their sharing and retention pending further guidance.
Answer “Can I put research data into an AI tool?”
Only when the specific data-use permission, institutional controls, and provider contract permit it. “The tool is free,” “the provider says it does not train on inputs,” or “the data are difficult to identify” is not a substitute for checking consent, data-use agreements, IRB conditions, privacy obligations, sponsor terms, and the actual retention and access settings. Redaction may reduce risk, but it does not automatically make a dataset non-sensitive; combinations of metadata can still identify people or reveal confidential work.
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3. Examine provider terms and system security
Request written answers from the provider and your institutional security team. Evaluate confidentiality, integrity, and availability for both the service and the data. NIST notes that conventional security frameworks do not comprehensively address AI-specific threats such as evasion, model extraction, membership inference, and availability attacks, so a familiar certification or checklist is not proof of zero risk.
Questions to put in procurement or a security review
- Retention: Are prompts, files, embeddings, logs, and outputs retained? For how long? Can the lab delete them, including backups?
- Training and improvement: Are inputs or outputs used to train or improve a model? Can that use be disabled both contractually and technically?
- Location and access: Where are data processed and stored? Which employees, contractors, subprocessors, or support systems can access them?
- Identity and permissions: Can the institution enforce single sign-on, least privilege, role separation, multi-factor authentication, and rapid account termination?
- Protection: What encryption is used in transit and at rest, and who controls the keys where relevant?
- Auditability: Are access, export, administrative, and model-use events logged, and can the lab retrieve them?
- Incident response: What is the notification timeline, containment process, evidence-preservation procedure, and support contact after a breach or suspected misuse?
- Availability and recovery: What backups, service-continuity commitments, outage procedures, and export options protect the lab from losing access?
- Change management: How are model, feature, policy, and subprocessor changes announced? Can the lab pin or record a version?
- Testing: Does the provider disclose vulnerability reporting, independent testing, and known limitations without presenting them as a guarantee?
Look for AI-specific failure paths
Prompt injection can cause a system connected to files or tools to follow hostile instructions embedded in a document. Model extraction and membership-inference attacks can expose information about a model or its training data. Evasion can make a detector miss a deliberately altered input, while an availability attack can interrupt a time-sensitive workflow. Ask what the provider does about these paths, but keep your own access controls and review process in place even when the answers sound reassuring.
4. Design verification, integrity, and reproducibility into the workflow
AI assistance becomes a research-integrity issue when an unverified statement, calculation, image change, citation, or code path enters the scientific record. NIH advises researchers to confirm accuracy and describe AI use in applications, manuscripts, and presentations.
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A practical verification sequence
- Preserve the input context. Save the permitted prompt, relevant files or hashes, date, user, tool name, model or feature version, and important settings.
- Check sources. Open every cited paper or dataset. Confirm that it exists, that the cited passage supports the statement, and that the citation has not been fabricated or misattributed.
- Recalculate material results. Re-run statistical calculations, unit conversions, formulas, and numerical summaries independently. Do not accept plausible-looking arithmetic.
- Validate code and analyses. Review for unsafe operations, hidden assumptions, data leakage, licensing issues, and reproducibility. Run tests and compare results with a known or independently implemented method.
- Inspect images and media. Keep the original, record every transformation, and determine whether an edit changes interpretation. NIH specifically calls for disclosure of image-editing processes.
- Record exceptions and sign off. Note rejected outputs, unresolved uncertainty, and the person who approved the final version. Preserve enough information for another researcher to reproduce the check.
NIH and HHS Office of Research Integrity staff state: “Appropriately cite references and carefully review and confirm information is accurate.” The same guidance says to “Clearly describe in applications, manuscripts, and presentations the use of the AI tools” and to “Disclose any specific image-editing processes used.” Follow the more demanding rule when a journal, funder, institution, or collaboration agreement requires additional disclosure.
5. Check rules that attach to the research context
Human participants and health information
Ask the institutional review board, privacy office, and research-compliance officials whether the proposed processing fits the protocol and consent language. NIH’s policy overview identifies 45 CFR 46, Certificates of Confidentiality, and HIPAA as potentially relevant to research involving people or health information. Applicability depends on the project, data, and institution; a general provider promise cannot decide it for you.
Grant applications
For NIH submissions, a July 2025 notice says an application or section substantially developed by AI is not considered the applicant’s original idea. The notice also limits covered principal investigators and program directors to six new, renewal, resubmission, or revision applications per calendar year, subject to stated exceptions, for applications with receipt dates beginning September 25, 2025. NIH cited evidence that AI tools had enabled principal investigators to submit more than 40 distinct applications in one submission round; that figure illustrates the policy concern and is not a prevalence estimate for all laboratories. Do not apply these NIH limits to another funder without checking that funder’s current rules.
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Scientific peer review
NIH scientific peer reviewers may not use generative AI to analyze or formulate critiques of grant applications and research-and-development contract proposals. Other journals, agencies, and review systems may have different restrictions, so check the governing agreement before uploading a review or confidential proposal.
Biological research and security oversight
NIH describes biosafety and biosecurity oversight for covered work involving biological agents, toxins, or nucleic-acid molecules. A unified U.S. policy for certain federally funded life-sciences work took effect May 6, 2025, and NIH encourages institutional oversight of some in-silico research. Ask the institutional biosafety, biosecurity, or research-security office whether the proposed model, data, and outputs fall within the applicable scope.
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Clarify confidentiality, ownership and licensing of inputs and outputs, reuse rights, attribution, open-source obligations, and whether generated code can be incorporated into a regulated or commercial research tool. Institutional legal counsel or technology-transfer staff should review the actual provider contract; NIH identifies these as relevant policy areas but does not determine a vendor’s terms.
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6. Run a bounded pilot, then set expansion gates
A pilot should test the proposed use under permitted conditions, not expose sensitive data merely to see what happens. Use representative but authorized examples, keep the current workflow as a comparison, and log both errors and time saved. This is an implementation decision for your lab, not proof that the tool performs at a universal rate.
- Choose a narrow task and a fixed evaluation period.
- Use synthetic, de-identified, or otherwise approved data when they can answer the question.
- Define pass/fail criteria for accuracy, harmful errors, reproducibility, latency, and staff workload.
- Require independent review of outputs that could affect participants, safety, funding, publication, or the scientific record.
- Document incidents, near misses, rejected outputs, provider responses, and unresolved limitations.
- Obtain written sign-off before expanding to more sensitive data, more users, automated actions, or higher-consequence decisions.
Reassess when the model, provider terms, subprocessors, data, user group, or research purpose changes. A tool approved for drafting a public-facing summary may not be approved for participant-level analysis or confidential peer review.
7. Compare viable tools on evidence, not marketing claims
| Comparison axis | What to examine |
|---|---|
| Task performance and verifiability | Performance on your representative task, worst-case errors, independent checking effort, and whether a qualified user can reproduce the result. |
| Data handling | Retention, training use, deletion, access, geographic processing, subprocessors, and contractual limits for your data class. |
| Security and response | Identity controls, encryption, logging, vulnerability handling, incident notification, backup, and service continuity. |
| Transparency and reproducibility | Model and feature versioning, change notices, exportability, prompt or configuration records, and disclosure support. |
| Policy fit | Compatibility with consent, IRB conditions, privacy requirements, funder and journal rules, peer-review obligations, and institutional policy. |
| Accessibility and downstream harm | Bias, unequal performance, accessibility for users, foreseeable misuse, and consequences for people represented in the data. |
| Integration and portability | Identity management, approved storage, APIs, audit systems, support quality, data export, and an exit plan if the service changes. |
| Total cost | Subscription or usage fees plus security review, integration, monitoring, training, validation, support, and the cost of correcting failures. |
No universal score or ranking can replace this use-specific comparison. A tool that wins on convenience may fail the data-use or reproducibility gate; a more restricted system may be the only acceptable option for a particular project.
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Record a short decision memo that names the task, permitted data, provider and version, contract owner, security findings, verification owner, applicable rules, pilot evidence, restrictions, and review date. Use one of three outcomes:
- Approve with conditions: the use passes data, security, integrity, and policy checks, with clear limits and a named owner.
- defer: a missing contract term, unresolved data permission, inadequate verification path, or incomplete institutional review must be fixed before use.
- Reject for this use: the data cannot lawfully or contractually be transferred, the output cannot be independently verified, or the consequences of known failures exceed the lab’s controls.
Revisit the memo on the date you set and whenever the provider changes its model, terms, storage, subprocessors, or incident history. Approval should attach to the defined use, not to the tool as a permanent label.
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