A zero-trust security architecture has a minimized threat surface and more control over authentication and user access. It is a reliable security approach for countering unauthorized access by passively and continuously verifying trusted users and devices. The zero-trust security model works on the premise that every device and user is a threat and must be authenticated each time it requests access to data, applications, or a network.
Behavioral biometrics is an emerging authentication technology that supports zero trust policies and can help your company achieve a zero-trust architecture. It monitors a users’ behavioral patterns when interacting with a system; These systems continuously monitor a user’s typing speed or mouse dynamics and use this data to establish and maintain a behavioral profile for each user. Each time a user logs into a system or account, the behavioral biometrics system matches the user’s characteristics, like pressure applied on keystrokes, typing speed, and mouse movement to the behavioral profile and confirms the users’ identity. This enables continuous and passive user authentication by utilizing behavioral data of specific users to confirm their identities throughout an active session.
In addition, the technology does not require proprietary hardware since it runs in the background, passively, as a user interacts with a system. Behavioral biometrics also builds on the user’s behavior over time, unlike traditional biometrics like fingerprint and iris scans where data must be stored to verify user identities during login, and can be stolen or replicated. These behavioral profiles evolve as the subtle behavioral patterns change such that a user must exhibit the specific behavioral traits or risk being locked out of a session. Similar to a zero-trust architecture, the slightest variation of user behavioral traits from the continuously updated behavioral profile denies access and blocks the user from accessing any part of the system, then alerts security teams of a potential threat.
As is the case with traditional biometrics authentication schemes, behavioral biometrics does not depend on a single reference point, but instead, runs passively throughout a session to confirm user identities. In case an attacker uses compromised credentials to log in to an account, a behavioral biometrics system running in the background can detect the signs of unusual behavioral characteristics, lock the intruder from the session, and notify the security teams. Since the technology is designed to work silently in the background, it monitors a user’s micro-behavioral patterns, builds a profile continuously, and analyzes the users’ unique patterns to differentiate between legitimate users and intruders.
For example, consider a user’s typing habits – the behavioral biometrics system creates the user’s profile based on factors like how long a user presses a key, keystroke patterns for specific sequences of words, and typing speed. Behavioral biometrics uses machine learning to create a user profile based on this data and uses the behavioral profile for continuous and passive identity confirmation in all active sessions. If the system detects anomalies and the user’s risk score exceeds the pre-defined risk score threshold, it denies the user from accessing data or applications and logs the user out from the account. Behavioral biometrics does not trust anyone or any device even if the correct credentials are provided and can only grant access if the user’s behavioral traits match the behavioral template built and updated with time.
How continuous identity authentication supports zero trust architecture
One of the primary benefits of behavioral biometrics is that it enables passive authentication. Identity authentication is, in fact, a double benefit since it prevents users from providing a fingerprint scan or password at each interaction as required in a zero-trust network model. It ensures that users are who they claim to be. Furthermore, behavioral biometrics and passive identity authentication increase security threat detection by logging out users from sessions if their behavior deviates from the learned behavioral characteristics of a user’s behavioral profile. Finally, behavioral biometrics data cannot be shared, lost, or replicated, which means that only the real user can be authenticated and granted access to secured networks and systems. This is key to supporting a zero-trust security model that permits only trusted users to access and interact with data and applications.
Behavioral biometrics also enables a continuous authentication trust model based on mouse, device, or keystroke dynamics. In particular, the trust level decreases or increases depending on the distance between the behavioral profile template and keystroke/mouse/device dynamics. That is, the larger the distance (difference in the users’ behavioral profiles and current behaviors when interacting with a system), the less the trust accorded to the user, and the more likely the behavioral biometrics system will lock out the user from a session. As a result, passive identity verification supports a zero-trust architecture since it ensures that only users whose behavioral factors match the behavioral profile are authenticated and granted access privileges. Users that fail to match the behavioral traits to the behavioral template cannot be authenticated. Furthermore, behavioral biometrics are unique to specific users and cannot be imitated, which means that a behavioral biometrics system can authenticate user identities continuously to meet the requirements of a zero-trust security model.
The following are some of the primary reasons that describe how passive identity authentication supports a zero-trust architecture:
A zero trust architecture only requires user access requests from a trusted and recognized user or device. Using traditional authentication methods can make it hard to enforce zero trust architecture in a network because they only require users to provide the correct authentication details even if they are compromised. Passive identity authentication based on behavioral biometrics can help your organization enforce zero-trust network policies
Identity trust enables granular access control by granting privileges based on user identity, role, or group. However, traditional identity authentication mechanisms are vulnerable and can be compromised easily, meaning it is difficult to enforce zero trust frameworks based on user identities. Fortunately, behavioral biometrics enhance protection from attacks, data breaches, and unauthorized access, rendering it as one of the most suitable approaches for enabling passive identity authentication, which is crucial to achieving a zero-trust architecture when verifying user identities.
Plurilock can help you achieve zero-trust security.
A zero-trust architecture is the most reliable approach to containing modern security threats. At Plurilock, we develop proven behavioral biometrics systems that authenticate users continuously and passively. Our patented technologies permit only trusted users and devices to access your network, which is made possible by our machine learning-based behavioral biometrics authentication solutions that validate all user interactions continuously.