Behavioral Biometrics: Enhancing Security or Invading Privacy?

How Your Devices Know You Better Than You Think

Behavioral biometrics is a rapidly advancing field of cybersecurity that analyzes unique patterns in human-device interactions. Primary use cases are to authenticate users and detect potential fraud. While this technology promises enhanced security without the friction of traditional authentication methods, it also poses significant threats to personal privacy. By passively monitoring a wide range of user actions, from typing patterns and mouse movements to touchscreen gestures and device orientation, behavioral biometrics systems collect and process vast amounts of sensitive behavioral data, often without the full awareness or consent of the individuals being tracked.

And it’s already in use.

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Mastercard has already implemented behavioral biometrics, utilizing sensor data from smartphones to create unique user profiles. As these systems become more prevalent across industries, it’s worth looking at the privacy implications of such extensive and continuous surveillance of our digital activity. And to also identify ways to implement countermeasures if you so choose

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What is Behavioral Biometrics?

Behavioral biometrics refers to the identification of individuals based on their unique patterns of behavior. Pysiological biometrics, on the other hand, relies on physical traits like fingerprints or facial recognition. Behavioral biometrics focus on how a person interacts with devices and systems. This includes keystroke dynamics, mouse movements, gait analysis, voice patterns, and even how one holds a phone or navigates a screen.

While not mainstream yet, behavioral biometrics are promoted as enhancing security, fraud detection, and improving user experience by ensuring that the person interacting with a system is indeed the authorized user. This method is considered more difficult to replicate or steal compared to physical biometrics. It poses clear privacy risks as well.

Behavioral Biometrics and Artificial Intelligence

Behavioral biometrics operate passively in the background of your device continuously monitoring and learning from the user's interactions. At a high level, machine learning algorithms analyze these interactions to create a behavioral profile that is unique to each user. When a user attempts to access a system, their current behavior is compared to the stored profile to verify their identity.

Artificial Intelligence therefore plays a crucial role in behavioral biometrics, enhancing its effectiveness and capabilities. As artificial intelligence capabilities improve, so will the prospects of behavioral biometrics. AI specifically contributes to behavioral biometrics in several ways: