What attacks does it stop?

Bot scripts, credential stuffing, remote desktop farms, and social engineering where the attacker behaves differently from the genuine customer due to tooling or divided attention.

How is privacy protected?

Collect derived features and scores rather than raw keystrokes or content. Disclose use, set strict retention, and allow fallbacks that do not disadvantage accessibility needs.

How often do models update?

Regularly, since behavior and devices change. Monitor drift, run challenger models, and recalibrate thresholds to maintain precision and reduce false positives.

It should not. Treat it as a strong silent signal that triggers step up or blocks when risk is elevated, alongside phishing resistant authentication methods.

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Behavioral Biometrics

Overview

Behavioral biometrics analyzes how users interact with devices and applications to distinguish genuine customers from bots or impostors. Signals include typing cadence, touch pressure, swipe dynamics, mouse movements, accelerometer patterns, and navigation flow. Models create profiles that are resistant to credential sharing and replay because behavior is hard to spoof consistently.

In onboarding and login, behavioral biometrics complements device fingerprinting, network intelligence, and step up factors to reduce account takeover and automated abuse. Programs must manage privacy by minimizing data, using on device feature extraction when possible, and retaining only derived risk scores. Explainability and monitoring are essential to detect drift, accessibility impacts, and potential bias. Combined with liveness and strong authentication, behavioral biometrics raises assurance with minimal user friction and improves detection of scripted or tool assisted attacks.

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