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Assess the model, dataset, documentation, and access method as one proposed release; identify the public or scientific benefit, consider plausible accidental exposure and deliberate misuse, compare ways of sharing, and document safeguards and decision owners. A practical review is staged: screen the proposal, examine higher-risk work in more detail, then choose whether to release publicly, restrict access, disclose in stages, or withhold sensitive material. There is no single release threshold that applies across jurisdictions or institutions, so involve the relevant biosafety and biosecurity experts and seek legal or regulatory advice for the specific case.
What does a biosecurity assessment cover for AI research?
A biosecurity assessment considers whether biological material, technology, or information could be deliberately misused, and what consequences might follow. Biosafety is related but distinct: it focuses on accidental exposure or release. A review of AI research should consider both, rather than treating a model or dataset as ordinary software or data detached from its biological context.
The World Health Organization’s Laboratory biosecurity guidance (2024) treats information and emerging technologies, including AI, as part of biosecurity management. It describes a lifecycle approach to preventing lapses and incidents across the handling of high-consequence biological material, technology, and information. That framing matters for sharing decisions: risk can arise from the material itself, from information that helps someone use it, or from how different parts of a release fit together.
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This is a governance process, not a universal test that produces a definitive safe-or-unsafe score. The relevant rules and oversight depend on the jurisdiction, institution, research context, and materials. WHO points readers toward applicable national and international legislation and regulatory oversight; consult your institutional biosafety or biosecurity committee, designated officers, and qualified legal or regulatory advisers.
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How do I assess biosecurity risks before sharing an AI model?
Use a staged review: establish the purpose and proposed release, screen for concerns, examine higher-risk cases with appropriate expertise, then select safeguards and a release decision. Record the reasoning, including uncertainty, rather than relying on an undocumented judgment or a numerical score.
- Define the proposed release. Describe the model or dataset, accompanying documentation, intended recipients, and access method. State whether the components would be shared together or separately, and consider what the combined package would make available.
- State the benefit and intended audience. Explain the scientific or public-interest value, who is expected to benefit, and why sharing is needed to realize that value. Consider the benefit of the proposed level of access, not just the value of the underlying research.
- Screen for plausible concerns. Consider accidental exposure and deliberate misuse, who could access the materials, and at a high level what the release could enable. Include low-probability, high-consequence possibilities alongside more likely outcomes; do not assume a scenario is irrelevant only because it is difficult to predict.
- Escalate when the screen raises concerns. Ask qualified biosecurity, biosafety, scientific, information-security, and governance experts to examine the case in more detail as appropriate. A funder’s screening framework can support an initial review, but it cannot substitute for specialist review where the activity falls outside its scope.
- Compare release choices and safeguards. Consider public release, controlled access, staged disclosure, or withholding sensitive portions. Match safeguards to the identified concerns and the practical ability to enforce them.
- Document the decision and its owners. Record the rationale, selected release conditions, residual uncertainty, responsible approvers, and the events or changes that would trigger another review.
This sequence synthesizes WHO biosecurity guidance, OECD responsible-AI due diligence, UK frontier-AI safety practices, and the National Academies’ 2025 overview of AI in the life sciences. NTI’s 2025 funder guidance also uses screening, more detailed review for higher-risk proposals, and mitigation as a practical decision-support sequence. It explicitly excludes AI-enabled biological design, synthesis, or optimization that could lead to physical biological outputs; those cases need additional applicable expertise and review, not an assumption that the NTI framework covers them.
What should the review record about the model and dataset?
Describe the release as an information package, not as a single file. OECD due diligence considers risk at the data level, the model level, and where the two intersect. A model and dataset can expose different information independently, while their combination or accompanying documentation may change what recipients can infer or do. The review should therefore make the package understandable to decision-makers who are not its developers.
Describe what is being shared
- Identify each component: model, dataset, documentation, and any other material included in the release.
- Describe the proposed access route and audience, including whether access is public or limited and what conditions apply.
- Explain which components are intended to be used together and what information their combination makes available.
- State the intended scientific or public-interest use and why the chosen release format is being considered.
These are review prompts, not a technical disclosure checklist. Keep the assessment at the level needed to decide on governance and release controls; do not add sensitive experimental, design, or misuse-enabling details to a document that itself may be widely circulated.
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Describe the context and plausible consequences
Consider the release in context: who may receive it, how broadly it could circulate, and what consequences might follow from accidental exposure or deliberate misuse. WHO calls for consequence-driven biosecurity assessment, while UK frontier-AI safety guidance says to weigh potential benefits against risks, including low-probability, high-consequence risks. A useful record distinguishes the possible consequence from how certain the team is that a scenario could occur.
Do not treat a lack of certainty as evidence that there is no risk. Record material uncertainties plainly and note what expertise or information would be needed to resolve them. The purpose is a defensible decision, not a false impression of precision.
Should I release the dataset, the model, or both?
Assess each component and their intersection before deciding. OECD’s approach recognizes data-level, model-level, and combined risks, and recommends tailoring disclosure to the audience. Whether to share one component, both, a restricted subset, or neither depends on the expected benefit, the information exposed, and the safeguards that can realistically be maintained.
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| Release choice | Breadth of access | Information exposed | Controls and reversibility | Benefit and practical trade-off |
|---|---|---|---|---|
| Public release | Broad availability | Depends on which components and details are published; consider the package as a whole | Public disclosure limits the ability to control later access or revise who has it | Can support transparency and collaboration, while also making information or capability broadly available |
| Controlled access | Limited to approved recipients | Can be tailored to the audience and purpose | Access conditions, traceability, and monitoring may be possible; their feasibility and limits need review | Can enable collaboration with restrictions, but adds access-management and oversight responsibilities |
| Staged disclosure | Expands in stages rather than at once | Can limit early disclosure and tailor what is shared at each stage | Provides opportunities to review before later disclosure; a stage does not itself guarantee effective control | May balance collaboration and caution, while requiring criteria and owners for progression |
| Withhold sensitive portions | Recipients receive only the material selected for release | Excludes portions judged too sensitive to share in the proposed context | Requires a reasoned decision about what can be withheld and who verifies the released package | May preserve some research value while reducing disclosure, though omitted material may limit use |
For each option, ask whether its expected benefit justifies the information exposed, whether the recipients and access controls fit the risk, whether monitoring or traceability is feasible, and whether safeguards can be revised if circumstances change. Openness and collaboration can support scrutiny and scientific progress; open availability can also spread useful information or capability. OECD describes tiered access and safeguards as possible governance approaches, while UK guidance cautions against public release of dangerous capability information and production details.
How should safeguards and accountability be chosen?
Choose safeguards in response to the review’s actual concerns. A safeguard is useful only if it is feasible for the institution or project to implement, monitor, and maintain. Record who owns each measure, what it is meant to address, and what uncertainty remains after it is applied.
- Access conditions: For a restricted release, define who may receive the material and the conditions attached to access. Consider whether the intended audience and purpose justify the proposed level of disclosure.
- Disclosure boundaries: Identify whether staged disclosure or withholding sensitive portions could preserve the intended benefit while reducing unnecessary exposure.
- Monitoring and traceability: Consider what can realistically be monitored and traced under the chosen access method, and the limits of that oversight.
- Decision ownership: Name the people or institutional bodies responsible for review, approval, implementation of safeguards, and reconsideration if conditions change.
- Residual uncertainty: Record what the team cannot confidently assess, why it matters, and whether that uncertainty changes the release decision.
These measures do not guarantee that a release is safe. They make the decision more accountable and help ensure that the selected controls correspond to the risks and benefits the reviewers actually considered.
When should an AI biosecurity review be repeated?
Treat the assessment as part of the research and release lifecycle, not a one-time gate. WHO places biosecurity in a lifecycle approach, and UK guidance recommends continual monitoring and reassessment after material changes. A decision made for one package, audience, or access method should not automatically be carried over when those conditions change.
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Set review triggers when recording the original decision. Depending on the case, a trigger might be a material change to the model or dataset, a change to the accompanying information or access method, a materially different audience, or a change in relevant oversight requirements. These are practical examples for institutions to adapt; the sources do not establish a single universal trigger list.
At reassessment, revisit the original benefit, package description, risk context, safeguards, and residual uncertainty. Confirm whether the current release still matches what was reviewed and whether the original decision owners or additional expertise need to be involved. A change in access conditions or intended audience can alter the decision even if the underlying model or dataset has not changed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should I do if the review cannot resolve a concern?
Do not turn uncertainty into an unqualified approval. Escalate the issue to the institutional biosafety or biosecurity committee, designated officers, and relevant domain experts; consult legal or regulatory advisers where the rules or obligations are unclear. If a concern cannot be resolved with feasible safeguards, consider a more restricted or staged release, withholding sensitive portions, or not releasing the material.
There is no globally applicable threshold in the cited guidance that dictates which of those choices is required in every case. The decision should reflect the expected benefit, plausible consequences, applicable oversight, and the limits of the proposed controls. Record why the chosen option is proportionate and who has authority to approve it.
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How do the main guidance sources fit together?
The sources address complementary parts of the decision rather than supplying one universal release formula.
- WHO, Laboratory biosecurity guidance (2024): Provides a consequence-driven, lifecycle view of biosecurity that includes biological information and emerging technologies such as AI, and points to applicable national and international oversight.
- OECD responsible-AI due diligence: Helps frame risks at the data and model levels and at their intersection, alongside the governance challenge of balancing open and restricted approaches and tailoring disclosure to audiences.
- UK Government, Emerging processes for frontier AI safety: Emphasizes assessment of benefits and risks, including low-probability, high-consequence risks, lifecycle monitoring, and caution around public release of dangerous capability information and production details.
- National Academies, 2025 overview of AI in the life sciences: Provides broader context for considering AI in life-sciences research.
- NTI, Guidance for Assessing Biosecurity and Biosafety Risks (2025): Offers a practical staged approach for funder review, with screening, more detailed review for higher-risk proposals, and mitigation. Its stated exclusion for AI-enabled biological design, synthesis, or optimization that could lead to physical biological outputs is important when deciding whether it applies.
Use these materials as governance support alongside institutional review and applicable law, not as substitutes for either. In particular, do not extend a source beyond its stated scope simply because it provides a convenient process.
Frequently Asked Questions
What is the difference between biosafety and biosecurity in AI research?
Biosafety concerns accidental exposure or release; biosecurity concerns potential deliberate misuse of biological material, technology, or information. A release review may need to consider both, but they are distinct questions.
Can I use a funder’s biosecurity screening framework for AI-enabled biological design?
Not on its own if the work involves AI-enabled biological design, synthesis, or optimization that could lead to physical biological outputs. NTI’s 2025 guidance explicitly excludes those cases, so seek additional applicable expertise and review.
Who should approve a decision to share biological AI research?
The appropriate approver depends on the institution, jurisdiction, and research context. Involve the relevant institutional biosafety or biosecurity committee and designated officers, with qualified legal or regulatory advisers when needed.
Is there a universal biosecurity score for deciding whether an AI model is safe to publish?
The cited guidance does not establish a globally applicable numerical score or release threshold. Document the reasoning, uncertainties, safeguards, and accountable decision owners instead of implying that a single score settles the decision.
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