Passive Behavioral Biometrics
To authenticate a person’s identity, passive behavioral biometrics analyze unique user behaviors, such as how they type, use a mouse, walk, or speak. These behavioral signals can be analyzed in the background during normal user activity, reducing the need for repeated authentication prompts or dedicated biometric actions.
What is Passive Behavioral Biometrics, and how does it work?
Passive behavioral biometrics analyze a person’s current behavior and compare it with behavioral samples or models collected in the past to help verify their identity. Unlike authentication methods that require a specific action, such as touching a fingerprint sensor, passive behavioral biometrics can collect and analyze behavioral signals during normal interaction without significantly interrupting the user’s experience.
The passive nature of behavioral biometrics allows the technology to be applied seamlessly across various use cases:
As an additional layer of authentication for Two-Factor or Multi-Factor Authentication (2FA & MFA).
As a secure method of Continuous Endpoint Authentication (CEA).
As part of identity and access management systems.
Passive behavioral biometrics can be used standalone or combined with additional authentication signals for greater confidence in the identity validation process. Examples of passive behavioral biometrics include:
Typing biometrics (aka keystroke dynamics) - looks at an individual’s typing pattern and analyzes characteristics such as the time it takes them to press, release, and move between keys. Learn more about how typing biometrics work.
Mouse dynamics - identifies patterns in user interactions with a mouse or pointer, including movement, speed, trajectories, pauses, and clicks.
The way the user holds their mobile device - can analyze characteristics such as the angle at which a user holds their phone, movement patterns, and other sensor-derived behavioral signals.
Gait recognition - analyzes characteristics of the way an individual walks. Depending on the implementation, gait biometrics can use cameras, wearable sensors, or motion sensors available in mobile devices.
Voice biometrics - analyzes characteristics of a person's voice and speaking behavior to help determine whether the speaker matches an enrolled user. Voice biometrics should not be confused with speech recognition, which focuses on understanding the words being spoken rather than identifying the speaker.
The maturity of passive behavioral biometrics has also been recognized by industry analysts. TypingDNA was named a Sample Vendor for Passive Behavioral Biometrics in the Gartner® Hype Cycle™ for Digital Identity in both 2024 and 2025. In the 2025 report, the category reached the Plateau of Productivity, the final stage of the Hype Cycle. By 2026, Passive Behavioral Biometrics was no longer included as a tracked innovation in the Digital Identity Hype Cycle, consistent with the category having matured beyond the Hype Cycle. Learn more about TypingDNA's Gartner recognition.
An example of a privacy-focused passive behavioral biometric solution is TypingDNA ActiveLock, which continuously verifies employees’ identities based primarily on how they type on company computers. ActiveLock works in the background and can also use mouse behavior and optional face verification as additional authentication signals. Biometric analysis is performed locally on the device, and the typing biometric data does not leave the computer. If ActiveLock detects an unauthorized user, it can lock the device and alert administrators.
Curious to see how passive behavioral biometrics can be used for Continuous Endpoint Authentication? Learn more about TypingDNA ActiveLock or contact us for a demo.
