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Neuromorphic computing enhances identity systems by mimicking neural networks for real-time behavioral biometrics (e.g., gait analysis, typing patterns). These systems process data locally on edge devices, improving privacy and reducing latency. Machine learning models trained on neuromorphic chips adapt to user habits, detecting anomalies (e.g., "Unusual Login Behavior") for continuous authentication. Applications include fraud prevention in banking and secure access to high-risk facilities.

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