Abstract: Systems and methods that enable implementation of content aware file management labeling. Techniques disclosed enable real-time analysis of a data file so that associations between the data file and applicable file management label(s) can be automatically suggested and/or made while a user is working in the data file. A user may deploy an application on a client device to edit a data file. While the user is actively editing the data file, the application may transmit modification data to a content analysis service which analyzes the modification data to determine whether the modifications result in a file management label becoming applicable to the data file. Ultimately, the content analysis service may transmit a verdict to the client device to cause the application to display a labeling suggestion to the user and/or to automatically apply a label to the data file while it is being worked on by the user.

Abstract: Techniques configuring a machine learning model include instantiating a user interface configured to communicate with a machine learning model hosted on a collaborative computing platform. A selection of a file for input to the machine learning model, a selection of content in the file for input to the machine learning model, and instructions for applying the selected content to the machine learning model are received and sent to the machine learning model. A selection of one or more directories and an instruction to apply the machine learning model are sent to the machine learning model.

Abstract: Techniques configuring a machine learning model include receiving, via a user interface configured to communicate with a machine learning model hosted on a collaborative computing platform, a selection of a file for input to the machine learning model, a selection of content in the file for input to the machine learning model, and instructions for applying the selected content to the machine learning model, which are sent to the machine learning model. As new files are uploaded to the selected directories of the collaborative computing platform, the machine learning model is applied to the uploaded files to classify the files and extract metadata. The extracted metadata and associated classification data are stored in data structures associated with the new files. The data structures are existing data structures of the collaborative computing platform.

Abstract: A unified document retention management system exposes a retention policy management portal to enable a user to selectively prescribe a unified retention policy to multiple productivity platforms to deploy the unified retention policy across the user selected productivity platforms. Data retention rules of the unified retention policy may define actions to be performed with respect to files that satisfy certain predefined conditions. An exemplary data retention rule prescribes a retention age threshold to files that have been tagged with a particular label. Files may be manually labeled by users and/or may be automatically labeled by the system in accordance with one or more smart label rules. The retention age threshold may be enforced for such tagged files by preventing the files from being deleted and/or modified prior to them reaching the retention age threshold. Then the files may automatically be purged from system storage upon reaching the retention age threshold.

Abstract: Techniques configuring a machine learning model include receiving, via a user interface configured to communicate with a machine learning model hosted on a collaborative computing platform, a selection of a file for input to the machine learning model, a selection of content in the file for input to the machine learning model, and instructions for applying the selected content to the machine learning model, which are sent to the machine learning model. As new files are uploaded to the selected directories of the collaborative computing platform, the machine learning model is applied to the uploaded files to classify the files and extract metadata. The extracted metadata and associated classification data are stored in data structures associated with the new files. The data structures are existing data structures of the collaborative computing platform.

Abstract: Techniques configuring a machine learning model include instantiating a user interface configured to communicate with a machine learning model hosted on a collaborative computing platform. A selection of a file for input to the machine learning model, a selection of content in the file for input to the machine learning model, and instructions for applying the selected content to the machine learning model are received and sent to the machine learning model. A selection of one or more directories and an instruction to apply the machine learning model are sent to the machine learning model.

Abstract: Systems and methods that enable implementation of content aware file management labeling. Techniques disclosed enable real-time analysis of a data file so that associations between the data file and applicable file management label(s) can be automatically suggested and/or made while a user is working in the data file. A user may deploy an application on a client device to edit a data file. While the user is actively editing the data file, the application may transmit modification data to a content analysis service which analyzes the modification data to determine whether the modifications result in a file management label becoming applicable to the data file. Ultimately, the content analysis service may transmit a verdict to the client device to cause the application to display a labeling suggestion to the user and/or to automatically apply a label to the data file while it is being worked on by the user.

Abstract: Systems and methods that enable implementation of content aware file management labeling. Techniques disclosed enable real-time analysis of a data file so that associations between the data file and applicable file management label(s) can be automatically suggested and/or made while a user is working in the data file. A user may deploy an application on a client device to edit a data file. While the user is actively editing the data file, the application may transmit modification data to a content analysis service which analyzes the modification data to determine whether the modifications result in a file management label becoming applicable to the data file. Ultimately, the content analysis service may transmit a verdict to the client device to cause the application to display a labeling suggestion to the user and/or to automatically apply a label to the data file while it is being worked on by the user.

Abstract: A data file is encrypted with a file-specific encryption key and sent to a remote data storage system. The file-specific encryption key is encrypted with a master key. The encrypted file-specific encryption key and the master key are both stored remotely from the encrypted file and they are stored remotely from one another.

Abstract: Techniques for labeling data files that enable a file management label to persist on a data file. Variations disclosed herein enable a file management label to be embedded within a data file so that the file management label propagates with the data file when the data file is copied and/or moved between systems and/or workloads. In some embodiments, an application may be configured to write the file management label directly into a stream of the data file. For example, an application client that is run locally on a client device may open the data file by accessing its corresponding stream. While the data file is open within the application client such that a user may edit content of the data file, the application client may be caused to associate the file management label with the data file. Ultimately, the application client may write this association directly into the stream.

Abstract: Techniques for labeling data files that enable a file management label to persist on a data file. Variations disclosed herein enable a file management label to be embedded within a data file so that the file management label propagates with the data file when the data file is copied and/or moved between systems and/or workloads. In some embodiments, an application may be configured to write the file management label directly into a stream of the data file. For example, an application client that is run locally on a client device may open the data file by accessing its corresponding stream. While the data file is open within the application client such that a user may edit content of the data file, the application client may be caused to associate the file management label with the data file. Ultimately, the application client may write this association directly into the stream.

Abstract: Systems and methods that enable implementation of content aware file management labeling. Techniques disclosed enable real-time analysis of a data file so that associations between the data file and applicable file management label(s) can be automatically suggested and/or made while a user is working in the data file. A user may deploy an application on a client device to edit a data file. While the user is actively editing the data file, the application may transmit modification data to a content analysis service which analyzes the modification data to determine whether the modifications result in a file management label becoming applicable to the data file. Ultimately, the content analysis service may transmit a verdict to the client device to cause the application to display a labeling suggestion to the user and/or to automatically apply a label to the data file while it is being worked on by the user.

Abstract: Information technology use policies based on classification of groups, teams, and sites is provided. Example systems may enable information technology administrators to specify which policies may apply based on manual or automatic classification of groups, teams and sites. This may include the ability to specify associated membership, sharing, and access policies, data storage and sharing locations, retention policies for different types of content, and application of various information governance and protection options/requirements. Information technology administrators, who inherently understand the data and information management needs of organizations and members, may define a reasonable set of simplified classification options that information workers can use. Thus, information workers may no longer need to learn the details of data policies.

Abstract: A data file is encrypted with a file-specific encryption key and sent to a remote data storage system. The file-specific encryption key is encrypted with a master key. The encrypted file-specific encryption key and the master key are both stored remotely from the encrypted file and they are stored remotely from one another.

Abstract: A file disposition review system analyzes multiple productivity platforms to generate an upcoming disposition report identifying data files that have reached a disposition review age associated with a file management label. A cross-platform manual review (CPMR) portal enables concurrent examination of file property data for data files identified in the upcoming disposition report. The CPMR portal further enables the disposition manager to simultaneously examine file property data corresponding to the data files identified in the upcoming disposition report and to issue disposition instructions. Exemplary disposition instructions may cause the multiple productivity platforms to permanently delete the identified data files without their underlying file content ever being transferred and/or copied from the particular productivity platforms that individual ones of the identified data files correspond to.

Abstract: A unified document retention management system exposes a retention policy management portal to enable a user to selectively prescribe a unified retention policy to multiple productivity platforms to deploy the unified retention policy across the user selected productivity platforms. Data retention rules of the unified retention policy may define actions to be performed with respect to files that satisfy certain predefined conditions. An exemplary data retention rule prescribes a retention age threshold to files that have been tagged with a particular label. Files may be manually labeled by users and/or may be automatically labeled by the system in accordance with one or more smart label rules. The retention age threshold may be enforced for such tagged files by preventing the files from being deleted and/or modified prior to them reaching the retention age threshold. Then the files may automatically be purged from system storage upon reaching the retention age threshold.

Abstract: Enhanced administrative tools for a unified file retention management system to enable selective activation of a data retention policy with respect to subsets of files across multiple productivity platforms. The system enables a user to selectively activate file management labels and/or file retention rules of a unified retention policy with respect to files that correspond to user defined filter parameters indicating subsets of files across user selected productivity platforms. Exemplary file management labels may prescribe a file retention period and a file purge age to files that have been tagged with a label. Files may be manually labeled by users and/or automatically labeled in accordance with smart label rules. The retention age threshold may be enforced for such tagged files by preventing the files from being deleted and/or modified during the file retention period. The files may automatically be purged from system storage upon reaching the file purge age.

Abstract: A data file is encrypted with a file-specific encryption key and sent to a remote data storage system. The file-specific encryption key is encrypted with a master key. The encrypted file-specific encryption key and the master key are both stored remotely from the encrypted file and they are stored remotely from one another.

Abstract: A data storage system includes a source database and a target database. A data isolation component is configured to identify content in the source database that will be moved to the target database. A data move component is configured to move the content identified in the source database to the target database. Upon completion of moving the content from the source database to the target database, the move component is configured to update a mapping database in a single operation such that data access request for the moved content are directed to the target database.

Abstract: A data file is encrypted with a file-specific encryption key and sent to a remote data storage system. The file-specific encryption key is encrypted with a master key. The encrypted file-specific encryption key and the master key are both stored remotely from the encrypted file and they are stored remotely from one another.