Neurotechnologies are rapidly advancing in technical capability and adoption across medical, research, workplace, and consumer environments. They offer significant benefits for individuals, including improved health outcomes and enhanced social and economic participation, particularly for people with disabilities, through new ways of communicating and interacting with digital systems and accessing services. However, the growing range and deployment of neurotechnology applications is accelerating the collection of data from the brain and nervous system at scale, including through consumer-facing devices. These technologies rely on neurodata, a heterogeneous category of data that may be highly sensitive, complex, and generated by developers, researchers, and providers across the aforementioned contexts, creating challenges for privacy protection and effective data governance. Policymakers face a near-term need to ensure that safeguards keep pace with the accelerating development and adoption of neurotechnologies while maintaining space for innovation.
Neurotechnologies are the subject of a combination of laws, regulations, standards and guidance frameworks for medical‑devices, data protection, cybersecurity, consumer protection, AI-oversight, and research ethics policies. However, divergence across jurisdictions and sectors creates fragmentation, inconsistent oversight and operational complexity for innovators and regulators alike. Because neurodata differ widely in their sensitivity, identifiability, and potential for inference, it is essential that policy responses offer proportionate safeguards that effectively address risks in each specific context.
This paper draws on evidence from a multi-stakeholder workshop and a study conducted with select neurotechnology companies, including interviews and a detailed mapping of data types, data flows, and risk points across the neurodata lifecycle. The analysis identifies the following key findings:
Neurodata governance requirements vary significantly depending on data type, level of processing and use context, limiting the effectiveness of a uniform policy approach.
Neurotechnology ecosystems frequently involve multiple actors, creating uncertainty regarding roles, responsibility and lawful bases for data processing
Layered data processing – from raw signals to inferred data – increases both the value of neurodata and of the associated privacy risks.
Existing regulatory frameworks provide broad but fragmented coverage, resulting in compliance challenges and potential gaps in protection.
These findings highlight the need to ground policy approaches in a granular understanding of different types of neurodata and their flow (how they are generated, processed, transferred, and stored), among developers, researchers, organisations, countries and individuals, rather than treating all neurodata as uniformly sensitive. Uniform approaches risk both over-regulations, potentially hindering innovation, and overlooking high-risk uses.
The analysis highlights several policy implications for strengthening neurodata governance. Neurodata vary in identifiability, classification, sensitivity, scope and consequent risks to privacy across collection and use contexts, modalities and various processing layers. Since neurodata may not always constitute personal health data, policy approaches may differ. Therefore, greater clarity, coordination and regulatory adaptability are needed to ensure that governance frameworks can keep pace with rapidly evolving neurotechnologies. Key priorities include clarifying neurodata frameworks, strengthening regulatory pathways for medical-device, data-protection and AI-enabled neurotechnologies, supporting interoperable infrastructure and standards, deepening multi-stakeholder and international cooperation, and creating fit-for-purpose pathways for platform-based and research-only neurotechnologies.
Collectively, actions in these areas would improve legal certainty, promote innovation, and help build trustworthy neurotechnology ecosystems. The following recommended policy actions translate these priorities into practical measures for governments and stakeholders.
Policy action 1: Clarify neurodata frameworks
Greater clarity is needed on how existing data protection frameworks apply to neurodata across different contexts and use cases. This includes providing sector-specific guidance on controller and processor responsibilities, particularly in complex multi-entity arrangements and secondary-use models and establishing clear pathways for lawful secondary uses of neurodata with appropriate safeguards. Regulators should also offer practical guidance on consent, purpose limitation and the treatment of inferred data while clarifying how neural signals and derived metrics should be classified based on their identifiability and context. At the same time, appropriate regulatory support for privacy-by-design approaches and privacy-enhancing technologies would help organisations manage risks more effectively. Developing risk-based taxonomies that distinguish between different data types and application contexts would further improve consistency, compliance and legal certainty across the ecosystem.
Policy action 2: Strengthen regulatory pathways for medical device, data protection and AI‑enabled neurotechnologies
Rapid advances in AI-enabled neurotechnologies are creating regulatory uncertainty, particularly where medical-device, data-protection and AI requirements intersect. To improve predictability and reduce barriers to innovation, regulators should expand opportunities for early dialogue with developers and provide clearer pathways for the assessment and approval of adaptive and AI-enabled systems. Greater coordination across regulatory domains would help reduce overlap, address compliance ambiguities and ensure that regulatory requirements remain coherent as neurotechnologies become increasingly data-driven and adaptive.
Policy action 3: Support interoperable infrastructure, datasets and standards
The development and deployment of neurotechnologies depend on access to high-quality data, interoperable systems and trusted mechanisms for collaboration. Policymakers should promote interoperable data standards, metadata requirements and interoperability frameworks to improve data quality, comparability and reuse across applications and jurisdictions. Investment in sustainable neurodata-sharing infrastructures, supported by robust governance and safeguards, would strengthen research and innovation, while mechanisms to increase the availability of diverse, high-quality datasets and enable cross-border data flows would help ensure that neurotechnology development is both effective and inclusive.
Policy action 4: Deepen multi‑stakeholder and international cooperation
Given the complexity and cross-border nature of neurotechnology development, effective governance requires sustained collaboration among a wide range of stakeholders. Structured mechanisms for participation, including working groups, consultations and regulatory sandboxes, can help ensure that policy frameworks reflect the perspectives of industry, researchers, healthcare providers, civil society organisations, standards bodies and affected communities. At the international level, closer cooperation and dialogue would help reduce regulatory fragmentation, improve predictability for developers and users, and support innovation across jurisdictions.
Policy action 5: Create fit‑for‑purpose regulatory pathways for platform and research‑only neurotechnologies
Existing regulatory frameworks may not adequately reflect the characteristics of emerging neurotechnologies, particularly devices that are software-driven, continuously updated or designed for multiple use cases. Regulatory pathways should therefore be modernised to accommodate implantable, upgradable and platform-based neurotechnologies that evolve over time through software updates and adaptive analytics. At the same time, proportionate oversight mechanisms for research-only neurotechnology devices would help reduce unnecessary regulatory burdens while ensuring that appropriate safeguards and standards are applied consistently throughout the research lifecycle.
Together, these measures can support the development of a trusted and innovation-enabling neurotechnology ecosystem with improved policy coherence, greater predictability for innovators, and optimal safeguards for the rights and interests of individuals.