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What can expose sensitive information?
Review the whole AI research workflow, not only the original dataset. Risk may remain in processed files and derived artifacts, even when direct identifiers have been removed.
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- Data: raw and processed records, labels, metadata, free text, linkage keys, and data dictionaries.
- AI artifacts: model weights or checkpoints, outputs, prompts, and tool settings.
- Operational material: logs, code, documentation, and credentials or other secrets accidentally included in files.
- Permissions: consent scope, ownership, data-use agreements, repository terms, and applicable laws or funder and institutional rules.
Assess models and outputs in their own right rather than assuming they inherit the dataset’s risk classification. The UK National Cyber Security Centre’s secure AI development guidance treats data, software, models, prompts, and logs as assets to protect and document.
How to plan a safe, useful release
1. Define what another researcher needs
State the intended scrutiny or reuse: for example, reviewing preprocessing, checking an evaluation, or trying a method on new data. Identify the minimum materials that let someone do that. A data dictionary, code, model and version details, preprocessing description, validation results, and evaluation protocol may be shareable even when records are not.
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Check consent, expected future use, funder requirements, repository rules, and institutional review before collection and again before release. NIH advises researchers to assess privacy protections even when data meet technical or legal definitions of de-identified. Its guidance is specific to NIH contexts; other projects must follow their own applicable terms. See NIH’s participant privacy principles and NOT-OD-22-213.
2. Minimize and assess the data
Remove or transform information that is not needed for the stated purpose, while preserving sufficient scientific utility. Names and other direct identifiers are only part of the picture: rare attributes, small geographic areas, combinations of indirect identifiers, free text, and linkage with outside datasets can make records recognizable.
De-identification is not a single checkbox. Document the method and assess residual risk for the intended release and likely linked information. NIH recommends considering controlled access separately from whether data are technically or legally de-identified. NIST’s SP 800-188 discusses governance, measurable standards, quasi-identifiers, and re-identification studies; its guidance concerns government datasets, but its framework can inform broader planning.
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3. Choose an access model for the remaining risk
Compare permissions, sensitivity, scientific utility, access governance, likely request volume, and whether the approach can be validated. These are options to evaluate, not a universal compliance ranking.
| Release approach | Potential fit | Checks before use |
|---|---|---|
| Open release after review | Data and artifacts whose residual risk and permissions allow broad reuse | Direct and indirect identification, linkage risk, consent, license, and foreseeable downstream use |
| Controlled-access repository | Reusable data that require requester review or restrictions on use | Eligibility and identity checks, permitted purposes, use agreement, audit, and oversight |
| Protected enclave or secure analysis environment | Highly sensitive data that should remain in an approved environment | Access controls, monitoring, output review, and institutional or repository governance |
| Query interface | Repeated analysis when users do not need raw records | Query limits, cumulative disclosure risk, output review, and fit to purpose |
| Synthetic data | Development, demonstration, or selected analyses when synthetic utility is adequate | Disclosure risk, fidelity for the intended use, clear labeling, documentation, and validation against protected data where available |
NIST describes these release models in SP 800-188. NIH also describes controlled-access repositories and sharing or use agreements in its data-sharing approaches. The right choice depends on the dataset and its governing terms.
4. Treat synthetic data as a release that needs review
Synthetic data are not automatically private or representative. NIST states that “Constructing synthetic data that faithfully represent all properties of the original data while enforcing strong privacy guarantees is impossible.” A synthetic dataset may retain selected relationships while omitting other properties; it does not provide a blanket privacy guarantee.
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Label the release as synthetic, describe the uses it can support and its known limits, and evaluate both disclosure risk and utility for those uses. Where possible, validate relevant analytical behavior against protected data without exposing the protected records.
5. Check whether the AI service is allowed to receive the data
Do not send restricted data to an external AI tool unless the responsible data owner and applicable agreements authorize that exact workflow. Check what the service does with inputs, outputs, and logs, as well as its retention and access terms.
A specific rule applies to NIH-controlled human genomic data: NIH’s NOT-OD-25-081, released March 28, 2025, says public generative AI tools must not receive controlled-access data under the notice’s non-transferability provisions. It also treats models and parameters developed with those data as derivatives and restricts sharing and retention under the specified terms pending further guidance. This is not a general prohibition for unrelated datasets; check the rules governing your own data.
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6. Document methods without publishing secrets
Give reviewers enough context to understand and assess the workflow, while keeping sensitive inputs, credentials, and raw logs protected or redacted. Where safe and permitted, document:
- AI tool, model, and version, plus the access date;
- data source and provenance, input description, and transformations;
- prompts or instructions, the output used, and the evaluation protocol;
- validation, human review, known limitations, failure modes, and relevant retention practices.
World Bank guidance on documenting AI use for reproducible research frames transparency as enabling a reviewer to understand the model, prompt, and validation. It also notes that stochastic behavior can prevent exact reruns, so describe the process and evidence rather than promise identical outputs. The guidance was last updated June 2, 2026. NCSC’s security guidance likewise recommends treating logs as sensitive and documenting sources, scope, limitations, and failure modes.
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If one component cannot be released safely, consider sharing other components that can be reviewed independently: code, aggregate results, documentation, or a controlled-access copy, for example. Separate files or artifacts by sensitivity, and set access controls and output review appropriate to each. OMB M-24-10 directs U.S. federal agencies to consider partial sharing and controlled infrastructure when unrestricted release is inappropriate, and calls for model-specific risk assessment. It is agency guidance, not a universal rule for research teams, but illustrates why a project need not treat the whole workflow as either fully public or wholly unavailable.
Quick Recap
Before you release anything
- Confirm consent, data-use terms, repository rules, and institutional or funder requirements.
- Check direct identifiers, indirect identifiers, rare combinations, free text, and linkage risk.
- Review derived artifacts—including models, prompts, outputs, and logs—separately from the dataset.
- Match access restrictions to residual risk and the intended scientific use.
- Clearly label synthetic material and explain its utility and limits.
- Document the methods needed for scrutiny while protecting sensitive inputs and secrets.
- Make a component-by-component decision and record who approved the release.
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