With the advancement of AI, the methodology to prevent cybersecurity breach has become uniquely human. Awareness of how machine learning algorithms target human cognition and the Neurocyber risks are crucial to the protection of both companies and their customers.
Devices that directly read from or write to neural activity.
Neurocyber risk: neural data exfiltration, signal manipulation, cognitive interference
Devices that infer mental or emotional states indirectly.
Neurocyber risk: behavioral profiling, affective manipulation
Immersive interfaces that shape perception and attention.
Neurocyber risk: perceptual distortion, memory priming, attention capture
Systems that adapt in real time to human behavior.
Neurocyber risk: trust manipulation, dependency formation, belief shaping
Systems that rely on cognitive or behavioral traits.
Neurocyber risk: identity spoofing, cognitive coercion, surveillance
Systems that subtly influence behavior through context.
Neurocyber risk: behavioral conditioning, passive influence
Interfaces optimized to shape desire and choice.
Neurocyber risk: subconscious manipulation, preference shaping
Systems handling sensitive cognitive data.
Neurocyber risk: psychological data leakage, behavioral targeting
Human–machine systems operating under cognitive load.
Neurocyber risk: attentional overload, decision manipulation, fatigue exploitation
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