Cyberark Software Ltd.

Splashtop Inc.

Yanlin Wang

Weizhi Li

Methods and systems of providing a universal privileged access management solution to enable users to securely share web applications through remote browser isolation (RBI). The web applications may be hosted in a SAAS (Software As A Services) cloud or in a private network. For privileged access of web applications that do not support SSO, the universal privileged access management solution implements cloud-based software services including Remote Browser Isolation (RBI), Zero Trust Network Access (ZTNA) and password vault. For privileged access of web applications that support SSO, the universal privileged access management solution implements cloud-based software services including Remote Browser Isolation (RBI) and ID brokers. In the access authentication process, by leveraging the data loss prevention (DLP) feature of RBI, the actual access credentials for accessing web applications are not disclosed to the user for mitigating security risks.

Universal privileged access for web applications through remote browser isolation

Methods and systems of risk assessment for network access control through data analytics. An embodiment of the invention employs well-known machine-learning clustering methods to learn normal entity behavior by looking for patterns in the events that stream in continuously. In an embodiment of the invention, normal entity behaviors are represented as clusters of event vectors. An embodiment of the invention evaluates the risk level for a new event of an entity by comparing the event with the entity's profile represented as clusters of event vectors. In an embodiment of the invention, the risk level is associated with a confidence level. Confidence level indicates how well the system knows about the entity. Embodiments of the invention do not need human administration in the process of building entity profile and assessing risk level of events associated with an entity.

Risk assessment for network access control through data analytics

A system and method for assessing the identity fraud risk of an entity's (a user's, computer process's, or device's) behavior within a computer network and then to take appropriate action. The system uses real-time machine learning for its assessment. It records the entity's log-in behavior (conditions at log-in) and behavior once logged in to create an entity profile that helps identify behavior patterns. The system compares new entity behavior with the entity profile to determine a risk score and a confidence level for the behavior. If the risk score and confidence level indicate a credible identity fraud risk at log-in, the system can require more factors of authentication before log-in succeeds. If the system detects risky behavior after log-in, it can take remedial action such as ending the entity's session, curtailing the entity's privileges, or notifying a human administrator.

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Cupertino, CA, United States of America

Yanlin Wang