Abstract: An automated software-as-a-service (SaaS) security posture management (SSPM) system disclosed herein detects and maintains security posture for SaaS applications according to correct implementation of configuration settings. Based on detecting a previously unseen SaaS application with unknown implementation of configuration settings, the SSPM system scrapes the Internet for web content for the SaaS application and preprocesses/inputs the web content into a machine learning model to obtain predictions of correct/incorrect implementation of configuration settings as output. Based on the predictions not having sufficiently high confidence, the SSPM system obtains additional application content by logging into the SaaS application and scraping locally rendered pages therein. The application content is preprocessed/input to the machine learning model to obtain additional high confidence predictions.

Abstract: Various techniques for providing automatic discovery of application administrator are disclosed. In some embodiments, a system, a process, and/or a computer program product for automatic discovery of application administrator includes automatically determining an administrator for an application (e.g., a Software as a Service (SaaS) application) associated with an organization by processing logs (e.g., firewall logs) for Uniform Resource Locators (URLs) visited by a user; matching the URLs for patterns that the administrator of that application visits using a machine learning (ML) model or based on known URL patterns; and sending the determined administrator for the application associated with the organization to an external service.

Abstract: One or more attendees joining a web conference are monitored. It is determined that one or more of the one or more attendees is an undesired attendee. In response to the determination, an action is caused to be performed.

Abstract: An automated software-as-a-service (SaaS) security posture management (SSPM) system disclosed herein detects and maintains security posture for SaaS applications according to correct implementation of configuration settings. Based on detecting a previously unseen SaaS application with unknown implementation of configuration settings, the SSPM system scrapes the Internet for web content for the SaaS application and preprocesses/inputs the web content into a machine learning model to obtain predictions of correct/incorrect implementation of configuration settings as output. Based on the predictions not having sufficiently high confidence, the SSPM system obtains additional application content by logging into the SaaS application and scraping locally rendered pages therein. The application content is preprocessed/input to the machine learning model to obtain additional high confidence predictions.

Abstract: An automated software-as-a-service (SaaS) security posture management (SSPM) system disclosed herein detects and maintains security posture for SaaS applications according to correct implementation of configuration settings. Based on detecting a previously unseen SaaS application with unknown implementation of configuration settings, the SSPM system scrapes the Internet for web content for the SaaS application and preprocesses/inputs the web content into a machine learning model to obtain predictions of correct/incorrect implementation of configuration settings as output. Based on the predictions not having sufficiently high confidence, the SSPM system obtains additional application content by logging into the SaaS application and scraping locally rendered pages therein. The application content is preprocessed/input to the machine learning model to obtain additional high confidence predictions.