AI / ML Behavioral Risk Modeling

SecureAuth’s patented Risk Engine is the heart of our technology platform and provides the most accurate risk scores in the industry.

Leveraging AI / ML, the risk engine monitors hundreds of variables to calculate whether an employee’s access attempt is legitimate or not by tracking user and device posture pre-authentication, during authentication and post-authorization.

SecureAuth’s Risk Engine leverages intelligence from the broadest range of sources including dozens of contextual and behavioral signals such as user location, user device, login velocity, browsers, cell phone network or ISP into the user profile engine to maintain an accurate user behavior profile.

Based on a risk score computed by our proprietary AI / ML algorithms, a dynamic level of assurance (DLOA) is computed.

Our approach automatically finds the optimal policy for each transaction to maximize security while minimizing friction for the user with machine learning and AI analytics. This provides a smoother user experience without sacrificing secure authentication.

The Dynamic Level of Assurance (DLOA), which is based on NIST standards, can also be used to increase friction (i.e. step-authentication) for the user based on their behavior and access level to various data in your enterprise.

The SecureAuth Risk Engine responds in real-time to potential risks with appropriate adaptive and continuous measures.

Connect to 3rd party threat intelligence solutions for a comprehensive risk score including PAM, SIEM, IGA, and UEBA to enhance risk analysis and improve your security posture with even stronger risk scoring.

• Advanced Threat Detection: AI / ML algorithms analyze vast amounts of data to detect and predict unusual patterns or behaviors that may indicate security threats. This approach helps identify threats early, reducing the likelihood of security breaches.
• Dynamic Security & Superior Experiences: AI / ML dynamically adjusts authentication requirements based on real-time risk assessments. Employees only face additional security measures only when their behavior or context raises suspicion, improving security without compromising user experience.