Neurodata governance

4. A way forward: Policy recommendations on neurodata governance and privacy

Regulatory approach is a structural determinant of innovation, market access, and public trust. Enhancing coherence across regulatory systems is essential to improve neurodata governance and privacy protection while enabling industry development and protection of individuals’ rights and interests.

The report shows that neurotechnology companies operate in a rapidly evolving and complex environment shaped by diverse business models, multi‑layered data practices, emerging AI capabilities, and varying regulatory expectations across jurisdictions. It also revealed that contextual factors, such as modality, identifiability, inference potential and use‑purpose, shape the risks and necessary safeguards.

Drawing on the evidence presented in this report, the following recommendations set out opportunities for regulators, policymakers, and industry stakeholders, aligned with recent OECD findings (OECD, 2025[11]) and efforts in countries to streamline the regulation and governance of emerging technologies, including neurotechnology (Regulatory Horizons Council, 2022[35]). The recommendations therefore aim to support governance approaches that are protective, adaptable and grounded in a nuanced understanding of how neurotechnologies operate in practice. Moreover, governance processes should be inclusive, transparent, and participatory, ensuring the meaningful involvement of relevant stakeholders, such as data protection authorities and other regulatory bodies, as well as civil society groups. This includes workers, trade unions, patients, disability organisations, children’s rights advocates, neurodivergent communities, consumer groups, and ethicists.

Clarify neurodata policy frameworks

Recommendation 1.1. Issue clear, sector-specific guidance on controller/processor role allocation and responsibilities

Ambiguity around controller/processor roles is a persistent challenge across clinical, research, enterprise, and consumer deployments. Clear, sector-specific guidance, particularly for multi‑entity models, secondary and downstream uses would support lawful processing and reduce compliance risks.

Recommendation 1.2. Define structures, purpose‑specific pathways for secondary use of neurodata

The clear determination of lawful bases for using neurodata in research, algorithm training or system improvement is needed. Providing structured pathways for secondary use, with appropriate safeguards and explicit purpose mapping, can enable scientific progress while preserving trust. Greater attention should be given to the tension between purpose limitation principles and the increasing demand for large-scale datasets to train advanced AI systems which may generate inferences beyond the original collection context.

Recommendation 1.3. Provide practical, case‑based guidance on consent, purpose limitation and secondary use including for inferred data

Concrete examples and scenarios will be helpful to operationalise lawful bases, including for inferred mental‑state data and algorithm‑derived outputs. Consideration could also be given to the use of dynamic or ongoing consent mechanisms, particularly in contexts involving secondary data use and AI-based longitudinal analysis, where purposes and data uses may evolve over time.

Recommendation 1.4. Clarify how neural signals and derived metrics are classified

Uncertainty remains regarding whether neural signals should be considered biometric, health, sensitive or general personal data. Consistent definitions, linked to identifiability, inference potential and context of use, would reduce regulatory ambiguity and support more predictable compliance planning.

Recommendation 1.5. Promote privacy‑by‑design and privacy‑enhancing-technologies (PETs) adoption

On‑device processing, federated learning, differential privacy and controlled‑access research environments can mitigate risks while enabling data utility. Regulatory support and technical guidance, where safeguards are proportionate to demonstrable risk, would facilitate wider adoption. The OECD Repository of PETs Use Cases can be a helpful tool in this regard.

Recommendation 1.6. Develop risk‑based taxonomies

To clarify obligations, distinctions may be drawn between different data types and use-contexts. Differentiating raw from processed neural signals, identified from non‑identified data, and clinical, consumer, and research‑only settings can support more precise interpretation of requirements. Such distinctions help avoid blanket classifications and enable proportionate, risk‑based guidance that reflects the specific characteristics of each data type and context of use.

Strengthen regulatory pathways for medical‑device, data protection and AI‑enabled neurotechnologies

Recommendation 2.1. Expand early‑dialogue channels between regulators and developers.

Early interactions with regulators provide clarity and reduce development uncertainty. Expanding comparable mechanisms across jurisdictions and across regulatory domains would support safety, privacy and innovation.

Recommendation 2.2. Provide explicit regulatory pathways for compliance and/or approval of adaptive and AI‑enabled neurotechnologies.

Uncertainty must be alleviated regarding model updates, closed‑loop systems, interpretation layers and adaptive features. Clear expectations for validation, monitoring, and change management would help ensure safe deployment. Clarity should be provided where AI functions as signal interpretation or clinical decision support rather than autonomous decision making.

Recommendation 2.3. Improve coherence between medical‑device, AI and data protection rules.

Multiple overlapping systems, medical‑device regulation, AI governance, data‑protection law, apply simultaneously to neurotechnologies. Cross‑regulatory coordination is needed to reduce compliance ambiguity, particularly for technologies that blend hardware, software and neural analytics.

Given that these regimes sit with different regulators under distinct legal bases, achieving coherence is operationally challenging. Governments and regulators could benefit from establishing instruments such as joint interpretative guidance and regulator‑to‑regulator dialogues.

Support interoperable infrastructure, datasets and standards

Recommendation 3.1. Promote interoperable data standards, metadata requirements and interoperability frameworks to improve quality and re-use

Inconsistent formats, insufficient metadata and limited interoperability hinder data reuse. Standardisation efforts would strengthen data quality, improve cross‑institution collaboration and reduce duplication.

Recommendation 3.2. Develop sustainable neurodata sharing infrastructures aligned with strong governance and safeguards

Existing initiatives remain small‑scale and fragile. Improving sustainability, governance and investment in secure repositories would enhance availability of diverse, high‑quality datasets while maintaining appropriate safeguards, in line with OECD best practice (OECD, 2021[36]; OECD, 2025[37]).

Recommendation 3.3. Establish mechanisms to increase availability of diverse, high‑quality training data

Limited access to training data constrains algorithm development and validation. Facilitating lawful cross‑border data flows, enabling neurodata reuse, and supporting high‑quality data generation would advance scientific and clinical progress.

Deepen multi‑stakeholder and international cooperation

Recommendation 4.1. Enable structured participation in developing policy frameworks

The engagement of relevant stakeholders, including industry, researchers, healthcare providers, civil society organisations, standards bodies and affected communities, in the development of policy frameworks through working groups, consultations or sandboxes (OECD, 2025[38]; OECD, 2024[39]), can help ensure guidelines are practical, evidence‑based and future‑proof. For example, companies can provide valuable expertise on neurodata, risk mitigation and technical feasibility.

Such multi-stakeholder engagement could be enhanced by early dialogues as well as the design of neurotechnology-specific regulatory sandboxes, enabling coordinated interaction between notified bodies, data protection authorities, and AI market surveillance authorities.

Recommendation 4.2. Foster international dialogue to reduce divergent approaches, improve predictability and support innovation

Differences between jurisdictions, especially between the US and EU, shape R&D and market‑entry decisions. International cooperation can improve predictability, facilitate innovation, and prevent unnecessary regulatory barriers.

Create fit‑for‑purpose regulatory pathways for platform and research‑only neurotechnologies

Recommendation 5.1. Modernise medical‑device pathways for implantable, upgradable and platform‑based neurotechnologies

Current medical‑device frameworks are not well aligned with neurotechnologies that evolve through software updates, have multiple use‑cases, or combine hardware and adaptive analytics. More flexible pathways would help reduce uncertainty and support innovation. Consideration should be given to establishing purpose‑appropriate and adaptive regulatory pathways for platform‑based and implantable neurotechnologies with evolving software components, including mechanisms to accommodate iterative upgrades and multi‑functional use.

Recommendation 5.2. Introduce proportionate oversight for research‑only neurotechnologies

The absence of clear research‑only regulatory pathways creates disproportionate compliance burdens for early‑stage work and leads to inconsistent ethics‑review decisions. Tailored pathways would support scientific progress while maintaining appropriate safeguards. This could include risk‑based, proportionate oversight frameworks that reduce burdens for research-only devices while ensuring consistent safeguards and greater coherence across jurisdictions.

Neurotechnologies offer substantial societal and clinical benefits. Their continued development depends on clear, coherent and future‑oriented policies. The recommendations above would promote alignment of innovation, privacy and safety expectations; strengthen regulatory certainty; and support a sustainable, trustworthy neurotechnology ecosystem.

Looking ahead, international co‑operation will be important, as neurotechnology development and associated data flows increasingly extend across borders. Shared approaches can help reduce uncertainty and support research and innovation.

Continued dialogue among governments, regulators, industry, researchers and civil society can help ensure that policy frameworks remain adaptive to scientific and technological advances, as well as evolving societal expectations. The OECD is an appropriate forum to carry these conversations. The recommendations in this paper could be further developed into concrete actions. This requires discussion and acceptance among OECD member countries.