Appgate, Inc. announced the availability of its behavioral biometrics service, which uses behavioral analysis and machine learning to identify and predict fraudulent activity online. As end users increasingly rely on digital platforms for online transactions, organizations need strong security controls to protect these interactions. However, traditional authentication practices can add complexity to the login process and may not provide enough protection.

Appgate's behavioral biometrics service extends the company's market-leading Zero Trust security portfolio by leveraging machine learning to observe and learn how end users interact with their keyboard and mouse during a given online session. The service is designed to identify anomalies and automatically adjust authentication strength to protect users and information. By learning how each user interacts with a digital platform, the service can identify malicious activity with a high level of accuracy, while also giving more confidence in genuine sessions, reducing the need for more intrusive forms of authentication.

Appgate's behavioral biometrics service allows organizations to set parameters for risk scoring and easily configure and adjust how they respond to risky logins. Depending on the risk score for a given session, the customer authentication system can prompt action such as step-up authentication.