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Evaluate AI-enabled biosecurity research as part of a broader biorisk governance process—not with a model score or a generic AI checklist alone. A sound review asks what the research is meant to achieve, what activities and information it involves, where and by whom it will be conducted, what harms could follow from accidents or misuse, and whether safeguards are proportionate, assigned to responsible people, and monitored.
The World Health Organization’s 2024 laboratory biosecurity guidance explicitly includes artificial intelligence among emerging technologies. Its 2022 global framework treats biosafety, biosecurity, and dual-use research oversight as shared responsibilities across the research life cycle. These are adaptable guides, not one-size-fits-all checklists; institutions still need to account for their setting and applicable rules.
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It should establish whether the proposed work has a clear scientific or public benefit, what risks arise from the work in its specific setting, and whether the people responsible can prevent, detect, respond to, and recover from foreseeable problems. The review should encompass the research activity and its outputs—including relevant information—not just the AI system in isolation.
AI is relevant to biosecurity because it can be part of work involving biological materials, technologies, or information. That does not mean every AI-enabled life-sciences project presents the same hazard. The WHO’s 2024 Laboratory biosecurity guidance calls for consequence-driven assessment across the life cycle of handling high-consequence biological material, technology, and information, and identifies AI as an emerging technology to consider.
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Keep the related governance concepts distinct while reviewing them together:
- Biosafety concerns risks of accidental exposure or release and other unintended harm arising from biological work.
- Laboratory biosecurity concerns protection against loss, theft, diversion, or intentional misuse of relevant biological materials, technology, and information.
- Dual-use oversight considers whether research with legitimate scientific aims could also enable harmful use, and what safeguards are warranted.
- Information risk considers whether research-generated information could contribute to harm if disclosed, disseminated, or misused.
These categories can overlap. For instance, a review may need to consider both the safety of the research setting and the consequences of information that the work generates. A risk rating alone cannot make the case for proceeding: reviewers also need to understand the expected benefit and the basis for judging that benefit significant.
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Use a documented, context-specific sequence: establish the value of the proposed work, define what the review covers, assess plausible consequences in the actual setting, then assign and monitor proportionate safeguards. A proposal review and an institutional capacity review answer related but different questions; neither should be mistaken for the other.
1. State the purpose and expected benefit
Describe the research question and the scientific or public benefit the work is expected to provide. The International Bio Funders’ Compact guidance places risk review after scientific-merit assessment and recommends applying it to shortlisted proposals with significant value. That ordering helps reviewers consider whether the proposed benefit justifies the work; it does not make risk controls optional or turn scientific merit into a safety finding.
Make the benefit statement concrete enough for a decision-maker to test. Identify who could benefit, what problem the work addresses, and what outcome is reasonably expected. If the benefit depends on later stages, say so rather than presenting a distant possibility as an immediate result. The aim is not to force a numerical valuation, but to ensure that a risk discussion has a clearly stated reason for considering the work.
2. Define the work, setting, and review boundaries
Record, at a level appropriate for the review, the AI system and life-sciences context involved, the proposed activities, the institution or setting, and which stages of the research life cycle are in scope. Identify the applicable jurisdiction, institutional review responsibilities, and relevant policies. State any boundaries openly: a framework that does not cover a material part of the proposal cannot serve as the complete assessment for that work.
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Also identify what the review does not decide. A governance assessment is not a technical protocol, a certification, or jurisdiction-specific legal advice. Keeping those boundaries visible prevents a completed form from being mistaken for approval of every operational or regulatory question.
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3. Identify consequences, not just hazard labels
Consider accidental safety risks, security risks, deliberate misuse, and information hazards. For each relevant scenario, ask what the possible consequence could be and whether the setting can prevent, detect, respond to, and recover from it. WHO’s 2024 laboratory guidance supports consequence-driven biosecurity assessment and includes information security and emerging technologies in its scope.
Keep the assessment tied to the proposed work and its setting. A risk label without a plausible consequence or a connection to the activities under review is difficult to turn into a useful decision. Conversely, an assessment that considers only laboratory operations can miss relevant risks associated with research-generated information.
A practical scenario record can ask four sequential questions:
- What activity, material, capability, or information could create the exposure?
- What accidental, security, deliberate-misuse, or information event is plausible in this context?
- What consequence could follow, and who or what could be affected?
- Which existing capability would prevent, detect, respond to, or help recover from that consequence, and where is the gap?
This format keeps the review consequence-driven without pretending that a single score captures every uncertainty. It also helps distinguish a weakness in laboratory practice from a weakness in access control, information handling, oversight, or incident response.
4. Weigh expected benefit alongside plausible harm
Make the benefit and risk reviews visible, then bring them together when deciding whether to proceed and what mitigation is proportionate. A 2026 scholarly proposal describes an upstream review for biological AI models involving a trigger for review, parallel benefit and risk reviews, integration, and proportionate mitigation. Because only the article’s search-result summary was accessible, treat this as a proposed approach, not settled consensus or a universally adopted requirement.
The proposed sequence is useful as a decision design: first identify what would trigger a review, then examine benefits and risks in parallel, integrate the findings, and select controls that match the situation. It should not be presented as a replacement for WHO guidance, national rules, or an institution’s existing committees. If the proposal changes substantially, the trigger and the integrated conclusion should be reconsidered rather than carried forward automatically.
5. Choose controls and make ownership explicit
For each mitigation, state who is responsible, what decision or operational change it requires, and how the institution will check whether it is in place and working. Responsibilities may sit with different participants across the research life cycle; WHO’s 2022 global framework emphasizes shared responsibility among researchers, institutions, funders, regulators, publishers, and other stakeholders. Naming an accountable owner makes a control actionable; naming a control without an owner does not.
Separate a control from its verification. For example, an institution may decide that a particular access, information-handling, or review measure is needed; the record should also identify who confirms that the measure was implemented and what evidence is acceptable for that confirmation. This makes it possible to distinguish “planned,” “implemented,” and “needs correction” without implying that a checklist by itself proves effectiveness.
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Controls should be proportionate to the identified consequence and the setting’s capacity. A review should explain why the chosen measure addresses the gap, what residual uncertainty remains, and when the decision will be revisited. The available guidance supports proportionality and monitoring, but it does not prescribe one universal control set for every AI-enabled project.
6. Recheck scope and revisit the decision
Before relying on a framework or checklist, compare its coverage with the actual work. Revisit the assessment when the technology, intended use, setting, or available evidence changes. A framework is an input to a decision, not a substitute for continuing institutional oversight.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallReassessment is especially important when a project moves from analysis to a stage that could affect physical biological outputs, when access expands to new collaborators, or when information is prepared for wider dissemination. The review should record what changed and whether the earlier benefit, consequence, and mitigation judgments still hold. This is consistent with a lifecycle approach and avoids treating the first approval as permanent.
What should reviewers look for in a framework?
Compare frameworks by what they cover and what they enable an institution to do. A useful review asks whether the framework considers the relevant risks and context, relates benefits to harms, assigns proportionate safeguards, clarifies responsibilities, and supports follow-through.
| Review dimension | Question to ask | Why it matters |
|---|---|---|
| Scope | Does it cover biosafety, biosecurity, dual-use concerns, and information risks? Does it address the specific AI-enabled work under review? | A checklist with a material scope gap cannot be treated as a complete assessment. |
| Context | Does it consider the proposed work, the setting, applicable rules, and institutional capacity? | The same general technology label does not establish the same consequences or safeguards in every setting. |
| Lifecycle | Does it address the stages at which materials, capabilities, or information are created, transferred, used, or released? | Risk and responsibility can change as work moves between teams, institutions, funders, publishers, and other stakeholders. |
| Benefits and risks | Does it consider scientific or public benefit alongside plausible harm? | Decision-makers need both sides to judge whether the work should proceed and what mitigation is proportionate. |
| Mitigation | Are controls proportionate to identified risks, assigned to responsible people or bodies, and checkable? | Controls need an owner and a way to track implementation to be operationally useful. |
| Governance | Are responsibilities clear across research teams, institutions, funders, and relevant authorities? | Shared responsibility works only when participants can identify their roles. |
| Monitoring | Can the institution identify gaps, prioritize actions, track progress, and revisit its assessment? | A review should support follow-through rather than end at a written rating. |
| Adaptability | Does the guidance explain how to adapt it to national and institutional contexts? | WHO’s materials are intended for adaptation, while national guidance may have a defined jurisdiction. |
These dimensions synthesize WHO’s governance and institutional self-assessment materials, funder proposal-review guidance, and the limited accessible description of the 2026 scholarly proposal. They are questions for evaluating coverage, not a claim that any single framework satisfies every dimension for every project.
Which guidance covers AI-enabled biological design?
Check exclusions directly before applying a framework to AI-enabled design, synthesis, or optimization work. The International Bio Funders’ Compact funder-review guidance, published in December 2025, expressly excludes AI-enabled biological design, synthesis, or optimization that could lead to physical biological outputs. Its guidance page says a separate framework is forthcoming, so the page should not be treated as complete coverage for those cases.
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For AI-enabled work involving possible physical biological outputs, make that scope gap explicit and identify the additional oversight that applies in the relevant jurisdiction and institution. Do not infer that a general-purpose AI, dual-use, or proposal-review checklist covers this work merely because it covers some adjacent risks. The exclusion is a reason to seek the applicable additional review, not a reason to stop evaluating the project’s other biosafety, biosecurity, dual-use, and information dimensions.
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- WHO’s 2022 global guidance framework addresses mitigation of biorisks and governance of dual-use research across stakeholder groups.
- WHO’s 2024 laboratory biosecurity guidance provides lifecycle-oriented guidance, includes AI among emerging technologies, and calls for consequence-driven assessment.
- WHO’s Biorisk Risk and Effectiveness of Institutional Tool (BRIET) is a voluntary institutional self-assessment and capacity-strengthening tool.
- The International Bio Funders’ Compact guidance, December 2025 supports funder proposal review, subject to the stated exclusion for certain AI-enabled work that could lead to physical biological outputs.
- The Canadian dual-use guideline, March 2026 is government guidance on assessing and managing dual-use potential in life-sciences research involving pathogens, toxins, or other biological material. Its stated national context matters when deciding how it applies elsewhere.
WHO reported on 29 September 2026 that more than 1,000 learners had enrolled in its multilingual WHO Academy training course supporting dual-use research governance and responsible innovation. That is a reported course enrollment figure, not a measure of institutional readiness or proof that an organization has completed an assessment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can an institution make its review actionable?
Translate findings into assigned actions and a way to monitor progress. WHO describes BRIET as a voluntary tool for identifying institutional strengths and gaps, prioritizing action, and monitoring progress; data generated by the tool stay locally stored.
Use an action record alongside the proposal or institutional review. For each identified gap, capture the issue, the decision or operational change required, the responsible person or body, and how progress will be checked. Add the relevant lifecycle stage and the date or condition that should trigger reconsideration. This is a practical way to apply WHO’s shared-responsibility approach without assuming that a tool itself makes decisions or replaces the institution’s existing review responsibilities.
Keep the limits of each instrument visible in the record. A self-assessment tool can help an institution recognize and prioritize gaps; funder guidance can inform proposal review; national guidance can inform assessment within its jurisdiction. They are not interchangeable, and using more than one does not automatically establish that every risk has been addressed.
BRIET’s local storage of generated data can also matter to an institution’s information-governance planning. It does not remove the need to decide who may access the assessment, how findings are shared with responsible parties, or how unresolved gaps are escalated. Those decisions remain part of the institution’s governance arrangements.
What are the most common evaluation mistakes?
Reviews become unreliable when they treat a broad label or a single framework as proof of safety. Avoid these errors:
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- Reviewing the model but not the work. Assess the proposed activities, their setting, relevant information, and lifecycle—not only the AI system’s description.
- Checking risks but ignoring benefits. State the expected scientific or public value so decision-makers can weigh it alongside plausible harm.
- Using a checklist beyond its scope. Verify that the tool covers the actual AI-enabled work, particularly where design, synthesis, or optimization could lead to physical biological outputs.
- Calling a risk score a decision. A rating does not explain why the work should proceed, what controls are needed, or who will implement them.
- Leaving responsibility implicit. Specify accountable people or bodies and how implementation will be checked.
- Assuming one jurisdiction’s guidance applies everywhere. Identify the relevant national and institutional context rather than treating adaptable global guidance or Canadian government guidance as universal law.
- Confusing training with readiness. Course participation can support awareness, but enrollment does not demonstrate institutional capacity or effective safeguards.
- Ignoring information hazards. A project can create risk through information even when the physical laboratory activity appears controlled.
- Ending the review when the document is filed. Revisit the decision if the work, intended use, setting, or evidence changes, and use monitoring to track identified actions.
The WHO frameworks are guidance for governance and adaptation, not jurisdiction-specific legal advice. Institutions should determine the applicable local obligations and connect any AI-related review to their existing responsibilities for biosafety, biosecurity, and dual-use oversight.
What questions should a review panel ask?
A panel can use the following questions to test whether a submission is complete without turning them into a mechanical pass-or-fail score:
- What is the specific scientific or public benefit, and why is it significant enough to warrant review?
- Which AI capabilities, biological materials, technologies, and information are involved at each lifecycle stage?
- Which accidental, security, deliberate-misuse, and information scenarios are plausible in this setting?
- What consequences could follow, and which people or populations could be affected?
- What can the institution prevent, detect, respond to, and recover from today?
- Where are the capability gaps, and which proportionate actions address them?
- Who owns each action, how will completion be checked, and when will the decision be revisited?
- Does the selected framework actually include the proposed AI-enabled activity, or is an additional oversight route required?
These questions keep the review anchored to value, context, consequence, proportionality, responsibility, and monitoring—the elements that make a governance decision useful rather than merely descriptive.
Frequently Asked Questions
Does using BRIET certify that a laboratory is safe?
No. WHO describes BRIET as a voluntary self-assessment and capacity-strengthening tool for identifying strengths and gaps, prioritizing actions, and monitoring progress. The description does not present it as a certification or accreditation.
Is WHO’s AI biosecurity guidance a legal requirement?
The WHO materials discussed here are guidance, and the global framework is intended to be adapted to national and institutional contexts. Whether a particular activity has binding requirements depends on the rules and oversight applicable to that institution and jurisdiction.
Does the WHO Academy enrollment figure show that institutions are prepared?
No. WHO’s 29 September 2026 update reports more than 1,000 learners enrolled in a multilingual training course. Enrollment is a participation figure; it does not by itself establish an institution’s capacity or the effectiveness of its safeguards.
Can a proposal review replace ongoing institutional oversight?
No. Proposal review can inform a funding or proceed decision, while institutional governance must also assign responsibility and follow implementation. WHO’s global framework treats biorisk governance as a shared responsibility across the research life cycle.
Which framework covers AI-enabled biological design, synthesis, or optimization?
The International Bio Funders’ Compact guidance published in December 2025 expressly excludes AI-enabled biological design, synthesis, or optimization that could lead to physical biological outputs and says a separate framework is forthcoming. Confirm what additional oversight applies in the relevant institution and jurisdiction.
Should an AI review consider information risks if no biological material is transferred?
Yes. WHO’s laboratory biosecurity guidance includes information security and emerging technologies, and a review should consider whether research-generated information could contribute to harm if disclosed, disseminated, or misused.
How often should an AI-enabled biosecurity assessment be repeated?
There is no single interval stated in the guidance summarized here. Revisit it when the technology, intended use, setting, or available evidence changes, and monitor the actions identified in the assessment.
Is the 2026 biological-AI risk-benefit proposal an established standard?
No. The accessible description proposes a trigger for review, parallel benefit and risk reviews, integration, and proportionate mitigation. It should be treated as a proposed approach rather than settled consensus or a universally adopted requirement.
The Bottom Line
A reliable evaluation connects scientific value to context-specific consequence analysis, proportionate safeguards, clear ownership, and continuing monitoring. Check every framework’s scope—especially for AI-enabled design, synthesis, or optimization—and adapt the review to the institution and jurisdiction.
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