Approaches to ingesting information regarding digital activities performed across different services and using the same for detecting threats

Abstract

Introduced here is a network-accessible platform (or simply 'platform') that is designed to monitor digital activities that are performed across different services to ascertain, in real time, threats to the security of an enterprise. In order to surface insights into the threats posed to an enterprise, the platform can apply machine learning models to data that is representative of digital activities performed on different services with respective accounts. Each model may be trained to understand what constitutes normal behavior for a corresponding employee with respect to a single service or multiple services. Not only can these models be autonomously trained for the employees of the enterprise, but they can also be autonomously applied to detect, characterize, and catalog those digital activities that are indicative of a threat.