Adopt an AI tool only after you can answer five questions in writing: what research task it will perform, what data it will receive, how the provider protects that data, who verifies the result, and which institutional, funder, publication, privacy, or safety rules apply. A tool can be useful for literature triage, coding, transcription, image analysis, or drafting while still being unsuitable for a particular dataset or decision. Make the decision for the specific use, data class, and research context—not for the brand or model in the abstract.
1. Define the research task before looking at products
Write a bounded use case
Describe the proposed activity in one sentence that names the users, inputs, outputs, and downstream decision. “The lab will use an AI assistant to suggest search terms for a literature review” is a different risk than “The system will classify participant images and determine eligibility for a study.” Other common uses include coding assistance, transcription, image analysis, translation, data cleaning, and drafting sections of a manuscript or grant.
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Record what the tool is not allowed to do. For example, it may prepare a draft that a researcher edits, but it may not make a clinical recommendation, release a participant-facing result, submit a grant, or alter a figure without review. This boundary prevents a low-risk convenience use from silently becoming a consequential decision system.
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Set an acceptance test and failure budget
Before a pilot, specify the accuracy or usefulness you need and the failures you cannot tolerate. Decide which errors are recoverable (such as a missed search synonym) and which could invalidate a result, expose confidential information, mislead a participant, or create a safety problem. Define the evidence that makes an output acceptable: a primary-source citation, an independently rerun calculation, a passing code test, or agreement with a qualified human analyst.
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Assign a named researcher who can verify outputs without relying on the system itself. Also decide what must be recorded to reproduce the work: prompts, input versions, output files, model and feature versions, edits, exceptions, and approval dates. UNESCO’s human-centered approach and NIH guidance both point toward this kind of human validation rather than treating generated content as self-authenticating.
2. Map every input, output, and restriction
Inventory what leaves the lab
Make a data-flow inventory before anyone uploads a file. Include prompt text, attachments, metadata, account identifiers, derived data, logs, generated outputs, and any data sent through plug-ins or connected applications. Mark whether each item contains:
- Participant identifiers, protected health information, or information covered by a consent or privacy notice.
- Controlled-access human genomic data or other restricted datasets.
- Unpublished findings, negative results, draft manuscripts, or grant material.
- Confidential collaborator, sponsor, peer-review, or contractual information.
- Trade secrets, patent-sensitive concepts, or software subject to institutional ownership.
For every category, check the consent form, data-use agreement, IRB protocol, institutional policy, sponsor contract, and applicable law. NIH warns that sending research inputs to an external AI provider can create an unauthorized disclosure even when the service’s general terms appear permissive. The relevant question is not simply whether a website accepts the upload; it is whether your authorization covers that transfer, processing location, retention period, and onward access.
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Handle controlled-access genomic data separately
Under the NIH Genomic Data Sharing Policy and its Data Use Certification, controlled-access human genomic data may not be transferred to unauthorized entities. NIH states that placing such data into a public generative-AI interface can violate the certification’s non-transferability provision. Approved users’ AI models can also be treated as data derivatives, with limits on sharing and retention pending further guidance. If your project uses these datasets, obtain a determination from the responsible data-access and institutional officials before any prompt, file, or derived model leaves the approved environment.
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Check whether outputs become regulated or confidential records
An output can carry obligations even when the input was de-identified. A generated analysis may become part of a study record; a transcription may contain identifying context; a draft figure may be submitted to a journal; and source code may embed restricted data. Map where outputs are stored, who can download them, and whether they are copied into laboratory notebooks, electronic records, repositories, or grant systems.
3. Examine provider security and AI-specific attack surfaces
Ask for concrete controls, not a marketing claim
Review the service with your institutional security or procurement team. Request written answers to the following questions and keep the answers with the adoption record:
| Area | Questions to document |
|---|---|
| Retention and training | Are prompts, files, outputs, and logs retained? For how long? Can the lab delete them? Are they used to train or improve a model, and can that use be disabled contractually and technically? |
| Location and subprocessors | Where are data processed and stored? Which subprocessors, support personnel, or connected services can access them? |
| Identity and access | Can the institution require single sign-on, multifactor authentication, role-based permissions, least privilege, and prompt account termination? |
| Confidentiality | What encryption protects data in transit and at rest? How are keys managed, and who can administer them? |
| Integrity and auditability | Are access, downloads, administrative actions, and model responses logged? Can the lab export logs for an investigation? |
| Availability and recovery | What backup, outage, disaster-recovery, service-level, and data-export arrangements protect continuity? |
| Incidents and vulnerabilities | How are breaches reported? What testing, vulnerability disclosure, and remediation processes are available? |
| Change management | How are model, feature, API, and terms-of-service changes announced? Can the lab pin, approve, or record a version? |
NIST emphasizes evaluating confidentiality, integrity, and availability for both systems and data. It also warns that conventional security frameworks do not comprehensively address AI-specific threats such as evasion, model extraction, membership inference, and availability attacks. A certification or completed checklist is therefore evidence about particular controls, not proof that the tool has zero risk.
Limit exposure during normal use
Use the least data needed for the stated task. Remove direct identifiers where the protocol permits, keep sensitive work in an institutionally managed environment, restrict browser extensions and plug-ins, and prevent automatic synchronization to personal accounts. Treat a prompt as a disclosure event: the text itself, an uploaded spreadsheet, and a pasted error message may all reveal protected information.
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4. Build verification, documentation, and transparency into the workflow
Verify claims and references
Check every citation, quotation, statistic, and factual assertion against the primary source. Confirm that the cited work exists, actually supports the statement, and was not misrepresented by a plausible-looking generated reference. NIH and HHS Office of Research Integrity staff state: “Appropriately cite references and carefully review and confirm information is accurate.” This check is required even when the output sounds authoritative.
Recalculate analyses and test code
Run important calculations independently, inspect units and assumptions, and compare results with a known method or an analyst who did not generate the first answer. Execute generated code in a controlled environment, review dependencies and licenses, test edge cases, and examine whether hidden data were logged or transmitted. For image analysis, retain the original, inspect masks and transformations, and document any threshold or preprocessing choice.
Control image editing and unattributed text
NIH identifies fabricated citations, undisclosed image alteration, misleading generated content, and unattributed copying as possible research-integrity problems. Preserve an edit history and state what the system changed. NIH guidance also says to “Disclose any specific image-editing processes used.” If generated prose, code, images, or analyses materially contributed to a manuscript, presentation, or application, describe that contribution according to the destination’s rules.
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For each material use, record the tool and version, date, account or workspace, purpose, input and output identifiers, human edits, verification performed, errors found, and the person who approved the result. NIH advises researchers to “Clearly describe in applications, manuscripts, and presentations the use of the AI tools.” Disclosure requirements differ by journal, funder, and institution, so consult each one rather than relying on a generic statement.
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5. Check rules tied to the research context
Human participants and health information
Ask the institutional review board, privacy officer, and research-compliance staff whether the proposed processing fits the informed-consent language, protocol, data-management plan, and applicable health-information conditions. NIH’s policy overview highlights 45 CFR 46, Certificates of Confidentiality, and HIPAA as relevant areas for research involving human information. Their applicability is project-specific; an AI vendor’s terms do not replace an IRB or privacy determination.
NIH grant applications
NIH’s July 2025 notice says an application, or a section of one, 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, with stated exceptions, for receipt dates beginning September 25, 2025. NIH cited evidence that AI tools had enabled principal investigators to submit more than 40 distinct applications in a single submission round. These are NIH-specific rules and observations, not a universal limit or prevalence estimate for every funder or laboratory.
Peer review and confidential proposals
NIH scientific peer reviewers may not use generative AI to analyze or formulate critiques of grant applications and research-and-development contract proposals. A reviewer must not paste a confidential application into a public assistant, even if the tool promises not to train on it. Other review systems may have different rules, so check the specific agency, journal, or conference policy before processing privileged material.
Biological research and in-silico work
NIH describes biosafety and biosecurity oversight for covered research 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 an AI-assisted design, analysis, or sequence task falls within the project’s controls.
Intellectual property and software
Clarify who owns inputs and outputs, whether the provider can reuse them, what confidentiality survives termination, and which license or indemnity terms apply to generated code or research tools. Involve technology-transfer and legal offices when an invention, patent filing, sponsor obligation, open-source dependency, or commercial license could be affected. NIH identifies intellectual property, licensing, and technology transfer as relevant policy areas but does not determine the terms of a particular vendor contract.
6. Run a bounded pilot before expanding access
- Choose permitted test data. Use representative material that your agreements and protocols allow you to process. Do not use sensitive participant, controlled-access genomic, confidential review, or unpublished sponsor data merely to make a pilot realistic.
- Freeze the test plan. Define the baseline workflow, success measures, unacceptable failures, reviewer, time window, and records to retain before seeing the tool’s answers.
- Log performance and exposure. Capture errors, corrections, hallucinated references, security events, time saved, manual effort, and any unexpected data movement. Do not present these observations as a universal benchmark; they describe your task and configuration.
- Review with the right officials. Route results to the principal investigator, data steward, security team, IRB or privacy office, and biosafety or technology-transfer staff when their domains are implicated.
- Approve a narrow deployment. Specify allowed users, datasets, features, retention settings, verification duties, and escalation contacts. Require sign-off before the tool is used for a more sensitive or consequential task.
- Reassess on change. Repeat the review when the model, provider, contract, processing location, connected feature, dataset, or research purpose changes.
7. Compare viable tools on evidence that matters
If two or more services survive the initial review, compare them using the same use case and records. Do not create a universal score or rank from a generic product demo.
| Comparison axis | What to establish |
|---|---|
| Task performance and verification | Performance on representative permitted examples, failure modes, and whether a qualified researcher can independently check results. |
| Data governance | Retention, training use, deletion, access, processing locations, subprocessors, and contractual protections for each data class. |
| Security and response | Identity controls, encryption, audit logs, incident notification, vulnerability handling, backup, and continuity arrangements. |
| Transparency and reproducibility | Version records, change notices, exportable prompts and outputs, documentation, and support for a reproducible research trail. |
| Policy fit | Compatibility with consent, IRB conditions, privacy obligations, funder and journal rules, peer-review confidentiality, and biosafety requirements. |
| Accessibility and harm | Foreseeable bias, accessibility barriers, downstream effects, and safeguards for people affected by the output. |
| Integration and portability | Identity management, approved storage, APIs, export options, support, and the ability to leave without losing records. |
| Total cost | Subscription or usage charges plus implementation, security review, training, verification time, storage, and exit work. |
8. Make a decision that can be defended later
Write a short decision record stating the approved task, prohibited uses, data classes, provider and version, security answers, responsible verifier, required disclosures, pilot evidence, unresolved risks, and approval authority. Choose among three practical outcomes: approve for the named low-risk use, approve only with controls such as an institutional workspace or de-identification, or reject until a requirement is met. A “safe” label without those boundaries is not a meaningful research decision.
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