In a recent University College London (UCL) study, scientists harnessed artificial intelligence to determine which neurons help us think, move, and form memories.
This breakthrough paves the way for devices that could read and interpret those same brain signals, meaning neural interfaces could one day advance beyond research labs into practical applications. We’re talking fitness trackers that show not only your step count, but also the internal struggle that results in putting off your workout another day. Or AI note takers that reveal that while you should have been watching a marketing presentation, you were mentally calculating your grocery list.
Though consumer-facing applications could still be years away, enterprise leaders need to prepare for both the opportunities and liability risks presented by neural interface technologies. When the same systems that could revolutionize healthcare, enhance human performance, and create seamless human-computer interaction make our thoughts as transparent as fitness tracker stats or AI-generated meeting minutes, organizations must consider how they’ll prioritize neural data privacy.
This guide covers everything enterprise leaders—especially those developing or deploying neural interface technologies—need to know to build proper safeguards.
The promise and the problem of neural data collection
Neural data includes the electrical signals, brain waves, and neural firing patterns that neural interfaces can detect and record. Unlike emotional data, which systems infer from external indicators like facial expressions and voice patterns, neural interfaces directly access brain activity through EEG devices, fMRIs, or other biosensors.
Neural data can reveal highly sensitive and unique details about an individual’s brain function, including health conditions, mental states, emotions, and cognitive abilities. Neural interface applications range from epileptic seizure prediction to hands-free device control, providing those with severe physical disabilities new levels of autonomy.
Where neural data invites controversy is around whether companies developing or deploying neural interfaces are engaging in deceptive or unfair practices. There’s also the question of how neural data fits existing privacy standards, including whether companies’ current disclosures meet federal requirements. These concerns have led California, Colorado, and Montana to pass neural privacy laws, with at least 15 additional bills pending across the country.
While states agree on the need for increased regulation, the resulting laws vary in definitions and protections. For example, California treats any central or peripheral nervous system data as sensitive personal information, while Colorado focuses on information collected through biosensors used for identification purposes.
Consider also that federal frameworks are still evolving, and companies find themselves in uncharted legal territory. Take Emotiv for example, the US neurotech company that faced a 2023 Chilean Supreme Court ruling for failing to give users adequate control over their neural data and allowing third-party transfers without explicit consent.
For enterprise leaders, neural privacy incidents represent a “when,” not “if” scenario, making proactive compliance and incident response planning necessary next steps.
The new privacy liability landscape
Neural data represents a whole new category of privacy risks for several reasons. First, it’s uniquely intimate, capturing thoughts, intentions, and subconscious responses that people may not even be aware of themselves. Second, it’s involuntary. People can control what they post on social media, for instance, but not their neural responses.
Third, neural data can reveal future health conditions, cognitive decline, or behavioral patterns that traditional data can’t predict, creating potential for discrimination based on conditions that haven’t even manifested yet. Lastly, neural data is permanent: unlike passwords or credit cards, you can’t change your brain waves if they’re compromised.
Brain-computer interfaces (BCIs) are the primary technology enabling the collection of neural data, ranging from non-invasive EEG headsets used in gaming and wellness applications to surgically implanted electrodes. The rapid advancement of BCIs—which has prompted the neural privacy laws mentioned earlier—raises three areas of concern:
Workplace: Just as employers currently monitor keystrokes, screen time, and even employee emotions through facial recognition software, neural interfaces could enable the direct monitoring of mental states, allowing AI algorithms to assess focus levels, stress responses, and cognitive performance in real time.
Healthcare: Building on existing vulnerabilities in connected medical devices from pacemakers to insulin pumps, implanted BCIs face the additional risk of “brainjacking,” where hackers could potentially manipulate the neural signals controlling assistive devices or corrupt AI systems that interpret neural data.
Consumer: Similar to how fitness trackers and smart speakers already collect behavioral data for targeted advertising, consumer BCIs could capture neural responses to products, content, and experiences, feeding AI systems that create unprecedented opportunities for manipulation through neurotargeted marketing.
These patterns of monitoring are known as neurosurveillance, or the use of neurotech to track and analyze brain activity in real time, and they’re growing more plausible as technology evolves. Because neurosurveillance raises questions about consent that existing frameworks don’t address, enterprises need new approaches to navigate this emerging landscape.
How to build more secure neural data frameworks
“Ask app not to track.”
“Allow location access?”
“This site uses cookies to improve your experience. Ok?”
Consent prompts like these highlight a familiar framework that enterprises have long relied on, but neural data renders this approach obsolete. While traditional consent processes assume users know what they’re agreeing to, these assumptions break down when the collected data includes subconscious thoughts.
To be clear, new frameworks are emerging—those neural privacy laws in Colorado, Montana, and California now require consent renewal every 24 months and separate consent for each new use. But the fact that they require “clear, freely given, informed, specific, affirmative” consent only underscores the challenges companies face.
To address neural data’s regulatory blind spots beyond legal compliance, enterprises developing or deploying neural interfaces should adopt proactive, ethical, and adaptive governance frameworks like the ones below:
Privacy-by-design and data protection impact assessments (DPIAs): Companies integrate privacy and security features from the earliest development stages, with teams conducting regular DPIAs to identify and mitigate neural data risks. For example, Colorado’s neural privacy law requires businesses to conduct data protection assessments specifically for neural data collection and use.
Neuroethical data governance frameworks: Frameworks that are grounded in neuroethical principles such as mental privacy, cognitive liberty, and neurodignity prioritize human rights beyond legal compliance. This can look like establishing participatory governance boards involving users, ethicists, and policymakers.
Data trusts and fiduciary models: These involve independent legal structures (e.g. data trusts) or fiduciary duties that require companies to act in the best interests of individuals whose neural data they manage. For example, data trusts can aggregate neural data from research cohorts while ensuring ethical use aligned with participants’ interests.
Organizational accountability and transparency programs: These programs involve comprehensive data privacy management that includes transparent practices, regular audits, clear documentation, and robust user rights (e.g. access, correction, deletion). For example, companies can use accountability frameworks like those from the Centre for Information Policy Leadership to guide their governance practices.
While no framework eliminates all risks, enterprises that adopt these approaches will demonstrate due diligence in addressing neural data’s unique challenges. This proactive stance will become increasingly valuable as regulations evolve and enforcement actions emerge.
Key takeaways
Enterprises developing and deploying neural interfaces are entering a landscape that’s evolving faster than the regulatory framework meant to govern it. And while states scramble to pass neural privacy laws, these companies can’t afford to wait for regulatory clarity.
Those that thrive in this new landscape will treat neural data privacy not as a compliance checkbox, but as a competitive advantage. By implementing robust governance frameworks before they’re required, enterprises can build user trust, avoid costly privacy incidents, and position themselves as leaders in responsible innovation.
