Neurodata governance
Working in technology has one funny side effect: every now and then I remember films from my childhood such as Johnny Mnemonic or The Lawnmower Man and think… we are getting uncomfortably close to some of those ideas.
Photo: rawpixel.com na Magnific
The new OECD - OCDE report “Neurodata governance: Evidence from innovators for public policy” is exactly this kind of reading.
Neurotechnology is moving into medical, research, workplace, wellness and consumer environments. The OECD mapped how neurodata is collected, processed, transferred, inferred and stored across increasingly complex ecosystems.
The ecosystem may include:
neural signals from implanted or wearable devices,
physiological and biometric information,
behavioural and contextual data,
AI-generated inferences about cognitive or emotional states,
medicaldevice and system metadata.
AI makes this even more interesting. Raw neural data can be transformed into increasingly valuable inferred information, potentially supporting conclusions about emotions, intentions, cognitive patterns or behaviour. Neurodata may also be reused for algorithm training, product development, profiling or analytics.
We are dealing with several layers of risk at once:
privacy and sensitive personal information,
AI training and secondary use,
re-identification and inference risks,
behavioural prediction and discrimination,
cybersecurity and integrity of devices and data,
in the broader neurotechnology context, potentially safety and human autonomy.
The OECD paper itself focuses primarily on privacy and data governance rather than the full ethical and safety dimension of neuromodulation. But even within that narrower scope, the regulatory picture is already impressive.
In Europe, depending on the product and use case, we may need to consider GDPR, the AI Act, MDR, NIS2, consumer-protection requirements, cybersecurity rules and research ethics. Other jurisdictions add completely different layers. Beautiful compliance simplicity.
OECD explicitly argues for context- and risk-based governance because sensitivity changes depending on the type of data, processing layer, identifiability, purpose and application.
This is exactly the type of topic I enjoy most. Not a simple linear problem with one cause and one consequence, but a network of interconnected organisations, technologies, people, data flows, regulations and controls. Something aviation taught me years ago: major incidents rarely result from one spectacular failure. Usually, many smaller elements align. Neurotechnology may become a perfect example of why Systems Thinking will be increasingly important in risk, privacy, cybersecurity and AI governance.
Author: Sebastian Burgemejster
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