Neural Data Security

Definition

Neural Data Security examines confidentiality, integrity and availability of neural signals, models and derived profiles within the broader domain of the privacy, ownership, inference, retention and protection of neural and mental-state data.

Why this matters

Failures concerning confidentiality, integrity and availability of neural signals, models and derived profiles can cause physical or psychological harm, loss of function, privacy invasion, identity compromise, exclusion, coercion or irreversible impact on human agency.

Research questions

Under which conditions can confidentiality, integrity and availability of neural signals, models and derived profiles be delivered, measured or protected reliably, and how do outcomes vary by person, device, duration, context and governance controls?

Hypotheses

A transparent, safety-bounded and person-centred approach to neural data security, combining validated measurement, informed consent, privacy and security controls, human oversight and longitudinal monitoring, will improve benefit–risk outcomes compared with opaque or technology-centred approaches.

Proposed methods

privacy threat modelling; neural inference testing; consent comprehension studies; data-flow mapping; security assessment; policy analysis; user-rights evaluation; expert and affected-user review; reproducibility testing; methods adapted specifically to Neural Data Security

Stakeholders and beneficiaries

neurotechnology users; patients; researchers; clinicians; privacy regulators; device manufacturers; employers; insurers; civil society