DETECTING ANOMALOUS DIGITAL ACTIONS UTILIZING AN ANOMALOUS-DETECTION MODEL

This disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that utilize a machine-learning model to detect mass file deletions, mass file downloads, ransomware encryptions, or other anomalous digital events within a digital-content-synchronization platform. For example, the disclosed systems can monitor digital actions executed across a digital-content-synchronization platform in real (or near-real) time and use a machine-learning model to analyze features of such digital actions to distinguish and detect anomalous actions. Upon detection, the disclosed systems can alert a client device of the anomalous actions with an explanatory rationale and, in some cases, perform (or provide options to perform) a remedial action to neutralize or contain the anomalous actions. Furthermore, the disclosed systems can also modify the machine-learning model based on interactions received from an administrator device in response to the anomalous actions.