Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing team-specific collections of collaborative content items. For example, the disclosed systems can determine relationships between teams of user accounts and various collaborative content items. Based on the determined relationships, the disclosed systems can identify which collaborative content items are germane to which teams and can provide the collaborative content items to user accounts accordingly. As part of determining relationships between collaborative content items and user accounts (or teams of user accounts), the disclosed systems can determine various information pertaining to the collaborative content items, including access patterns, sharing patterns, activity information, and geographic information.

Abstract: 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.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing team-specific collections of collaborative content items. For example, the disclosed systems can determine relationships between teams of user accounts and various collaborative content items. Based on the determined relationships, the disclosed systems can identify which collaborative content items are germane to which teams and can provide the collaborative content items to user accounts accordingly. As part of determining relationships between collaborative content items and user accounts (or teams of user accounts), the disclosed systems can determine various information pertaining to the collaborative content items, including access patterns, sharing patterns, activity information, and geographic information.