AI safety teams should share data only for a documented purpose, under an applicable legal authority, and with no more information or access than the task requires. Before sharing, identify the data and its restrictions, assess privacy and security risks, establish recipient and transfer controls, and record how long the recipient may keep it. The specific legal requirements depend on the data, the people and organizations involved, the AI system, and the jurisdictions.
Start with a decision, not a file transfer
Before sending data to an evaluator, research partner, vendor, or another team, write down what safety question the sharing is meant to answer. A purpose such as “test whether the model produces unsafe outputs on this defined evaluation” is more useful than a broad permission to “improve safety.” A narrow purpose helps determine which records, fields, and access are necessary—and which uses should be prohibited.
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Map the decision before access is granted:
- People and data: Whose information is involved, what categories it contains, how it was collected, and what quality limits or restrictions are known?
- Organizations and roles: Who decides why and how the data is used, who processes it on another party’s instructions, and whether any parties jointly determine the use? Labels in a contract do not by themselves settle legal roles.
- Route and purpose: Who will receive the data, where it will be stored or accessed, whether subcontractors or onward recipients are involved, and what the recipient is allowed to do?
- Authority: What law, agreement, license, consent, research condition, or other applicable authority permits this use and transfer?
Public availability does not establish that data can be reused without limits. Record the source and collection context, rights and restrictions, and the safety benefit expected from sharing. If the purpose or authority is unclear, pause rather than treating access as permission.
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Minimise the data and assess what it can reveal
Give the recipient only the fields, records, and level of access needed for the task. Depending on the evaluation, a carefully selected sample, aggregate results, a controlled query interface, or a secure environment may answer the question without transferring a full raw dataset. Removing direct identifiers can reduce exposure, but it does not automatically make information anonymous.
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Under the GDPR, when it applies, personal-data processing must follow principles including purpose limitation, data minimisation, storage limitation, integrity and confidentiality, and accountability. GDPR lawful-basis requirements apply to personal-data processing within its scope. Special-category data receives additional protection: the categories include racial or ethnic origin, political opinions, religious or philosophical beliefs, trade-union membership, genetic data, biometric data used for unique identification, health data, and data concerning sex life or sexual orientation. Processing such data requires an applicable Article 9 condition; ordinary permission to share a dataset is not enough. (European Union, GDPR, Articles 5, 6 and 9.)
Pseudonymised data is still a risk-bearing asset
Pseudonymisation replaces or separates direct identifiers, but a person may still be identifiable using additional information or by linking the dataset with other data. Under EU data-protection law, pseudonymised personal data remains personal data. Protect any re-identification key separately, restrict who can access it, and assess realistic linkage risks in the recipient’s context. Only genuinely anonymous data falls outside EU data-protection law; a transformation method alone does not prove anonymity. (European Data Protection Board guidance on pseudonymisation.)
Escalate high-risk processing before it begins
Apply heightened review where data is sensitive, people are vulnerable, or the processing may create significant risks to individual rights and freedoms. Where the GDPR applies, a data protection impact assessment (DPIA) is required before processing likely to result in high risk. If a DPIA identifies residual high risk that the controller cannot mitigate, the controller must consult the supervisory authority before proceeding. National requirements and the facts of a particular use can affect the assessment. (European Data Protection Board guidance on DPIAs.)
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For each recipient, document the permitted safety purpose, the people who may access the data, any restrictions on combining it with other sources, whether onward disclosure is allowed, and how long access lasts. Set a deletion or return point and specify how the recipient will confirm completion. Define a route for security incidents, suspected misuse, and requests to audit or verify compliance.
Under the GDPR, where a recipient acts as a processor for a controller, the controller must use a processor that provides sufficient guarantees. Article 28 requires a binding arrangement addressing prescribed matters. The parties should establish their actual roles and applicable requirements rather than assuming every external evaluator is a processor. (European Union, GDPR, Article 28.)
Security controls should match the risks and the data, not just the convenience of the transfer. GDPR Article 32 calls for measures appropriate to risk, taking account of the state of the art, implementation costs, and the nature, scope, context, and purposes of processing. Its examples include pseudonymisation and encryption; ongoing confidentiality, integrity, availability, and resilience; the ability to restore availability after an incident; and regular testing and assessment of safeguards. Practical controls may include named-user access, least privilege, secure storage and transfer, logging, and a defined incident-response path.
Handle international access as part of the transfer
For GDPR-covered personal data, assess the applicable Chapter V transfer route before making the data available outside the EU. Depending on the circumstances, the route may rely on an adequacy decision or safeguards such as standard contractual clauses (SCCs) or binding corporate rules (BCRs). A commercial contract alone does not answer every transfer question. Confirm the destination and recipient’s current status, and check the applicable requirements for the particular arrangement. (European Union, GDPR; European Data Protection Board guidance on international transfers.)
Map more than the location of the main server. Include remote support, subprocessors, administrative access, backups, and the routes by which authorities might request access. Where applicable, assess whether onward transfers are permitted and how the original safeguards will continue to apply.
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Use the right framework for the obligation
GDPR: binding duties when its scope applies
The GDPR is not a universal data-sharing code for every AI team. Its requirements apply according to its territorial and material scope. For covered personal-data processing, relevant duties include lawful basis, purpose limitation, minimisation, security, accountability, processor arrangements, records, DPIAs where required, and rules for international transfers. Confirm the applicable national implementation and enforcement details with counsel for a real decision.
EU AI Act: requirements depend on the actor and system
The EU AI Act allocates obligations according to the actor’s role and the relevant system or model provisions, with phased application. It does not create a blanket rule requiring every AI research dataset to be disclosed or shared. For example, Article 10 includes data and data-governance requirements for high-risk AI systems, while Article 53 requires providers of general-purpose AI models to draw up and make publicly available a sufficiently detailed summary of training content. Check current application dates, actor status, exceptions, and implementing materials before relying on a particular provision.
NIST AI RMF: voluntary operational guidance
NIST describes its AI Risk Management Framework as voluntary and intended for developers, users, and evaluators across sectors and borders. Its Govern function supports documenting legal requirements and accountability, managing third-party data risks, communicating risk, and establishing incident information-sharing practices across the AI lifecycle. It can help teams turn governance goals into repeatable controls, but it does not replace binding law. NIST released AI RMF 1.0 on 26 January 2023 and reports that it is being revised; check NIST’s current materials for version status.
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The OECD AI Principles, adopted in 2019 and updated in 2024, support respect for human rights and privacy, robust and safe AI, accountability, traceability, and representative datasets that respect privacy. OECD policy analysis also highlights differences between jurisdictions and practical governance challenges including privacy, bias, security, intellectual property, interoperability, and rights-holder engagement. These principles and analyses can inform policy design, but do not themselves impose one binding rule on every team.
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Share safety findings without exposing unnecessary information
Safety work may require sharing evaluation results, incident details, or evidence of a vulnerability with a partner or the public. Scope each disclosure to its purpose, recipient, and risk. Use severity-based escalation, redact unrelated personal or confidential information, and avoid including details that would unnecessarily enable exploitation. Preserve enough information for the intended recipient to investigate or act.
NIST AI RMF includes practices for enabling testing, incident identification, and information sharing. That is support for building an organizational process, not a universal incident-disclosure deadline. The applicable reporting or notification timeline depends on the event, the affected data and people, the parties’ roles, and the laws or contracts that apply.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep a compact decision record and revisit it when facts change
A concise record makes a sharing decision reviewable and helps prevent an approved evaluation from quietly expanding into a different use. Record:
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- Safety purpose, necessity, affected people and data categories, authority for the use, and any limits on reuse.
- Fields or records disclosed, transformations applied, access method, recipient roles, approved personnel, and any onward-sharing conditions.
- Risk review, approvals, safeguards, transfer route where relevant, retention period, and deletion or return date.
- Change history, incident contacts, and the conditions that trigger reassessment.
Review the decision when the purpose, data, recipient, system, access route, or applicable law changes. NIST emphasizes documented responsibilities, legal requirements, lifecycle governance, and monitoring; OECD principles support traceability of datasets and processes. These are governance reasons to keep the record current, not a substitute for determining legal obligations.
A practical approval test for an external evaluator
For a proposed evaluator transfer, the team should be able to answer each question before granting access:
- What safety question will this answer? State the intended evaluation and why the proposed data is necessary.
- What is the minimum useful disclosure? Consider a reduced dataset, sampled or aggregated data, or controlled access instead of a full copy.
- What restrictions attach to the data? Check collection context, applicable rights, sensitivity, and whether personal-data rules or other legal constraints apply.
- Who is responsible for what? Establish the parties’ roles and put permitted use, access, retention, incident handling, security, and onward disclosure into appropriate arrangements.
- Where can the data go? Map storage, remote access, support, subprocessors, and any international transfer requirements.
- How will the team know the arrangement still fits? Record approvals and deletion dates, monitor compliance as appropriate, and reassess if the task or access changes.
Approve sharing only when the expected safety value justifies the remaining privacy, security, rights, and misuse risks and the team can implement the required controls. The balance depends on the particular data and context; no single safeguard makes every transfer acceptable.
Scope and currentness
This guidance reflects official framework and regulator materials checked on 4 October 2026. GDPR applicability, national requirements, EU AI Act implementation, adequacy decisions, and regulator guidance can change or depend on specific facts. No general checklist can determine whether a particular dataset may be shared, whether a transformation achieves anonymity, or which incident deadline applies without details about the data, purpose, actors, jurisdiction, and event. For an actual sharing decision—especially one involving sensitive data, children, confidential research, high-risk processing, or cross-border access—obtain privacy and legal review.
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