Updated on February 18, 2025
Behavioral biometrics analyzes unique patterns in how users interact with technology, including typing rhythm, mouse movements, touchscreen pressure, and walking patterns. This authentication method distinguishes legitimate users from fraudsters by monitoring physical and cognitive behaviors during digital interactions.
As cybercriminals become more sophisticated at stealing passwords and personal data, organizations need authentication methods that go beyond what users know or possess. Behavioral biometrics provides a passive, continuous layer of security that works in the background while users naturally interact with devices and applications.
What are behavioral biometrics?
Behavioral biometrics measures and analyzes unique human traits such as physical movements and patterns during activities like typing, walking, or applying pressure while interacting with technology. Some have termed it a "sister science" to physical biometrics, which measures physical traits like fingerprints.
Two decades ago, the science of behavioral biometrics was in its infancy. Today, it has garnered the full attention of the identity management industry. Security vulnerabilities, transaction fraud, forgery, and identity theft have left all market sectors scrambling to find proper threat detection solutions.
Biometrics can short-circuit attempted attacks, mainly phishing hacks, and provide a solid level of security for consumers and enterprises supporting apps and network access for their employees.
Why behavioral biometrics matter for modern authentication
Almost all modern cybersecurity frameworks and regulations require multi-factor authentication (MFA) to strengthen identity verification and system access. MFA, as part of a broader Identity and Access Management (IAM) strategy, heightens security around authentication because it requires more from users than just a fragile and stealable password.
MFA schemas typically require users to provide at least two forms of identification from different categories:
Knowledge: Something the user knows or has memorized, such as a password, PIN, or passphrase associated with a security question.
Ownership: Something the user owns and possesses, like a One-Time Password (OTP) sent to their phone or email, or an OTP from a linked authentication app.
Inherence: Something the user is, which encompasses all types of biometrics.
With the increasing popularity of devices that include biometric scanners (cameras and fingerprint scanners being the most common), biometrics has become a default form of MFA alongside traditional passwords.
Understanding different types of biometrics
Biometrics as a category isn't monolithic. Different types of biometrics refer to different parts of human physiology, each addressing their inherent usefulness as an identity verification medium.
The primary categories of biometrics include:
Biological: Traits that are part of human physiology, intractable and usually requiring invasive collection methods. This includes blood samples and DNA signatures.
Morphological: Visible traits that are unique and relatively easy to verify, even if somewhat vulnerable to damage. These include facial recognition, fingerprint scans, and iris scans.
*Behavioral: Patterns of behavior we build over time, often unconsciously, as part of our daily lives. These include voice patterns, handwriting styles, typing rhythms, and gait patterns.
Each of these biometric types supports authentication to varying degrees. DNA signatures are perhaps the most unique and secure, though they represent an extreme measure outside the most high-stakes environments. Morphological traits are the most common forms of biometric authentication, often coupled with passwords for MFA or passwordless authentication systems.
Behavioral biometrics represents a rising conversation in the field. As advances in AI and machine learning fuel more accurate interpretations of behaviors, very common traits can be used to analyze user activity and determine authentication and authorization.
Types of behavioral biometrics
Modern AI and machine learning have made it possible to use subtle behavioral patterns as reliable authentication markers. Today's behavioral biometrics solutions can analyze a wide range of user interactions to verify identity and detect fraud.
Typing and keyboard behavior
Typing patterns reveal unique characteristics about how individuals interact with keyboards. This includes typing speed, rhythm between keystrokes, pressure applied to keys, and even which special keys or keyboard shortcuts are used. These patterns are difficult to replicate, making them valuable for continuous authentication.
Mouse and cursor movement
How users move their mouse cursor across a screen tells a unique story. This includes movement speed, acceleration patterns, click behavior, and whether the cursor ever moves off the page. AI-powered systems can detect signs of automation or unusual patterns that might indicate fraudulent activity.
Touchscreen interactions
Mobile devices offer rich behavioral data through touchscreen usage. This includes swipe speed and motion, pressure applied to the screen, gesture patterns, and how users hold their devices. These interactions happen naturally and provide continuous authentication without disrupting the user experience.
Body movement and gait recognition
Modern machines have become adept at using physical movements to determine an individual's identity. We all have fairly unique shapes and movement patterns across dozens of measurement points. Modern AI can use posture, weight distribution, gait, and walking patterns to determine if someone is who they say they are.
Handwriting recognition
Handwriting has long been used to identify individuals, but handwriting forgers have existed just as long. An authentication system powered by AI can identify genuine and fake handwriting far beyond the capacity of human investigators, analyzing pressure, speed, stroke patterns, and subtle variations.
Digital behavior patterns
This category may not seem like traditional biometrics, but anti-fraud experts have been using browsing and shopping patterns from online consumers to power machine learning algorithms. These systems can pick up on suspicious behavior that could potentially flag theft. While these behaviors exist in the digital world, they are considered biometrics precisely because they are tied to patterns that most people don't even know they display.
Key benefits of behavioral biometrics
Biometrics form the foundation of modern authentication because they are reliable, secure, and hard to fake. While not perfect technology, they come with several crucial benefits that security experts don't find elsewhere.
Passive and frictionless authentication
Behavioral biometrics are inherently passive, requiring system observation rather than directed input from the user. Rather than speaking into a microphone or providing fingerprint verification data, users simply do what they do naturally. The more relaxed and normal the behavior, the better the authentication works.
Continuous authentication throughout sessions
Continuous authentication is a relatively new and complex practice with several critical benefits. Rather than relying on a single point of authentication (or several points of authorization), a device can monitor the user and continuously determine whether the same user is present.
This avoids complicated authentication systems while providing liveness testing that prevents, for example, someone else from using a workstation or device without authorization.
Advanced fraud detection
Behavioral biometrics are critical to massive anti-fraud efforts. Machine learning helps observe shoppers online to see how they engage with digital storefronts, what kind of information they provide, and how they interact with sensitive areas of their accounts.
These anti-fraud efforts are critical for modern chargeback fraud prevention, where people buy items, report stolen cards, and have their banks reverse charges.
Seamless integration with other security measures
Because these biometrics are primarily passive, they work well with other forms of biometrics. For example, a user may provide iris biometrics to access a sensitive workstation equipped with monitoring tools covering typing behaviors to ensure that no one else takes over if the original, authorized individual walks away.
Reduced false positives
Traditional fraud detection methods often flag legitimate users who do something unusual, like logging in from a new location. Behavioral biometrics provides additional context that helps distinguish between genuine anomalies and actual threats, reducing false positives and improving the user experience.
Important considerations for behavioral biometrics
Not all types of behavioral biometrics are applicable to all use cases. Measuring and iterating on cost of deployment, accuracy and performance, false acceptance rates, and false rejection rates remain key items to monitor.
Additionally, not all forms of behavioral biometrics are created equal in their abilities to prevent fraud. Voice biometrics, for example, is relatively easier to spoof and is subject to replay risks. Voice is also subject to enrollment risks when there is background noise to deal with.
Organizations should carefully evaluate which behavioral biometric methods align with their specific security requirements and user experience goals.
How behavioral biometrics work in practice
Behavioral biometrics solutions typically follow a three-stage process:
Collection: Behavioral data is passively gathered while a user engages with a website or mobile app. This happens in the background without requiring any special actions from the user.
Analysis: AI-powered anomaly detection evaluates and contextualizes the user's behavior to help predict the level of risk behind the interaction, for both new and returning users.
Decision: Scores, reason codes, and raw data are provided to security systems, which can confidently automate risk decisions tailored to organizational needs.
This process happens in real time, allowing organizations to detect and respond to threats as they occur while maintaining a seamless experience for legitimate users.
Strengthen authentication with 1Kosmos
Organizations need more than single-factor biometrics to truly close security gaps and prevent sophisticated attacks. 1Kosmos combines advanced biometrics with passwordless authentication, identity management backed by a privacy-first architecture, and NIST-compliant identity proofing to deliver comprehensive protection that works seamlessly across your entire infrastructure. Our platform integrates behavioral and peripheral risk signals from partners like Behaviosec and RSA, enabling real-time fraud detection and adaptive authentication that steps up security only when needed, keeping friction minimal for legitimate users.
Contact us today to learn more or request a demo to see 1Kosmos in action.
