Abstract: Provided herein is a system for managing user entitlements to files and folders in a file server. The system is comprised of an entitlement manager UI system comprising an entitlement manager user interface (UI), an index server, and a file server. The entitlement manager UI is comprised of a permission pane that presents user permission access levels corresponding to a user associated with a file or folder, a permission activity log pane that presents audit trails of user permission changes wherein each audit trail comprises options to reverse the permission change, and a file activity pane presenting the activity levels of users having access to a file or folder. The entitlement manager UI further comprises a filter option wherein the user permission access levels for data objects stored in file servers that are in communication with the index server are controlled through the entitlement manager UI.

Abstract: Provided herein are methods for remotely managing user permissions through computing device comprising an index server in communication with a user interface executing on a second computing device and file server(s) comprising data objects wherein the file server is a third computing device different from the first and second computing devices. The index server comprises data structures containing information on user permission access to data objects that are stored on the index server. The index server communicates with the user interface to receive instructions for changes to user permission access levels and accesses its data structures and/or change logs to respond to such communications. The index server also communicates with the file server to execute the changes to the user permission access level for a user associated with a data object at the local level.

Abstract: An information management system is provided herein that uses machine learning (ML) to predict what data to store in a secondary storage device and/or when to perform the storage. For example, a client computing device can be initially configured to store data in a secondary storage device according to one or more storage policies. A media agent in the information management system can monitor data usage on the client computing device, using the data usage data to train a data storage ML model. The data storage ML model may be trained such that the model predicts what data to store in a secondary storage device and/or when to perform the storage. The client computing device can then be configured to use the trained data storage ML model in place of the storage polic(ies) to determine which data to store in a secondary storage device and/or when to perform the storage.

Abstract: An information management system is provided herein that uses machine learning (ML) to predict what data to store in a secondary storage device and/or when to perform the storage. For example, a client computing device can be initially configured to store data in a secondary storage device according to one or more storage policies. A media agent in the information management system can monitor data usage on the client computing device, using the data usage data to train a data storage ML model. The data storage ML model may be trained such that the model predicts what data to store in a secondary storage device and/or when to perform the storage. The client computing device can then be configured to use the trained data storage ML model in place of the storage polic(ies) to determine which data to store in a secondary storage device and/or when to perform the storage.

Abstract: A content analysis system of an information management system can analyze data for one or more data governance tasks. The content analysis system can reduce the overhead on the information management system when identifying sensitive data by analyzing a portion of the data in the file without analyzing the entirety of the file. The content analysis system may reduce overhead by analyzing a portion of files that include structured data. If the portion of the file that includes structured data does not include sensitive data, it is often the case that the entire file excludes sensitive data. Thus, overhead can be reduced by analyzing the portion of the file instead of the entire file. Further, the content analysis system can modify an information management job based on the determination of the inclusion of sensitive data to comply with data protection and privacy rules.

Abstract: An information management system is provided herein that uses machine learning (ML) to predict what data to store in a secondary storage device and/or when to perform the storage. For example, a client computing device can be initially configured to store data in a secondary storage device according to one or more storage policies. A media agent in the information management system can monitor data usage on the client computing device, using the data usage data to train a data storage ML model. The data storage ML model may be trained such that the model predicts what data to store in a secondary storage device and/or when to perform the storage. The client computing device can then be configured to use the trained data storage ML model in place of the storage polic(ies) to determine which data to store in a secondary storage device and/or when to perform the storage.

Abstract: An information management system is provided herein that uses machine learning (ML) to predict what data to store in a secondary storage device and/or when to perform the storage. For example, a client computing device can be initially configured to store data in a secondary storage device according to one or more storage policies. A media agent in the information management system can monitor data usage on the client computing device, using the data usage data to train a data storage ML model. The data storage ML model may be trained such that the model predicts what data to store in a secondary storage device and/or when to perform the storage. The client computing device can then be configured to use the trained data storage ML model in place of the storage polic(ies) to determine which data to store in a secondary storage device and/or when to perform the storage.

Abstract: An information management system is provided herein that uses machine learning to predict what data to recall from a secondary storage device and/or when to perform the recall. For example, a media agent in the information management system can generate and store context information when data recall requests are received from a client computing device, using the context information to train a recall machine learning model. The recall machine learning model may be trained such that the model predicts what data to recall and/or when to perform the recall. The media agent and/or the client computing device can then be configured to use the trained recall machine learning model to determine which data to recall and/or when to perform the recall.

Abstract: An information management system is provided herein that uses machine learning (ML) to predict what data to store in a secondary storage device and/or when to perform the storage. For example, a client computing device can be initially configured to store data in a secondary storage device according to one or more storage policies. A media agent in the information management system can monitor data usage on the client computing device, using the data usage data to train a data storage ML model. The data storage ML model may be trained such that the model predicts what data to store in a secondary storage device and/or when to perform the storage. The client computing device can then be configured to use the trained data storage ML model in place of the storage polic(ies) to determine which data to store in a secondary storage device and/or when to perform the storage.