Abstract: Systems and methods are described for managing high volumes of alerts to increase security, reduce noise, reduce duplication of work, and increase productivity of analysts dealing with and triaging alerts. A work unit queue may be configured to buffer or smooth workflows and decouple heavy processing which may improve performance and scalability to prevent duplicate assignments. Queueing services provide lag times to prevent over-assignment or double assignment of alerts to work units. System security may be improved by creating an authentication or verification step before allowing users to update alert statuses such that only users with work unit tokens that match alert tokens may update alert statuses.

Abstract: A computer-implemented method including scanning, by one or more processors, data that is maintained on one or more social networks, identifying, one or more social entities associated with the scanned data, determining a risk score for each of the one or more social entities, analyzing the scanned data using one or more machine learning techniques, assigning a rating to each of the one or more social entities based on the determined risk score and the analyzed data, and determining, based on the rating assigned to at least one of the one or more social entities that the at least one social entity is a security risk.

Abstract: A computer-implemented method including scanning, by one or more processors, data that is maintained on one or more social network profiles of a protected social entity, identifying, one or more social entities associated with the scanned data, determining a risk score for each of the one or more social entities, analyzing the scanned data using one or more machine learning techniques, assigning a rating to each of the one or more social entities based on the determined risk score and the analyzed data, and determining, based on the rating assigned to at least one of the one or more social entities that the at least one social entity is a security risk to the protected social entity.

Abstract: A computer-implemented method including scanning, by one or more processors, data that is maintained on one or more social networks, identifying, one or more social entities associated with the scanned data, determining a risk score for each of the one or more social entities, analyzing the scanned data using one or more machine learning techniques, assigning a rating to each of the one or more social entities based on the determined risk score and the analyzed data, and determining, based on the rating assigned to at least one of the one or more social entities that the at least one social entity is a security risk.

Abstract: A method may include a database storing a plurality of tasks, each task having a task ID, a date, a severity level, an enterprise ID, a status, and an assignment field; a user device; a processor; and a server configured to receive a work unit request from the user device, the request comprising a strategy for selecting tasks; create a work unit, the work unit having a state indicator; assign a token ID to the work unit; select a task from the database based on the strategy; add the task ID for the selected task to the work unit to create a populated work unit; change the status of the selected task to indicate the task is checked out; insert the token ID into the assignment field; send the populated work unit to the user device; and change the state indicator to indicate the work unit is checked out.

Abstract: A computer-implemented method including scanning, by one or more processors, data that is maintained on one or more social network profiles of a protected social entity, identifying, one or more social entities associated with the scanned data, determining a risk score for each of the one or more social entities, analyzing the scanned data using one or more machine learning techniques, assigning a rating to each of the one or more social entities based on the determined risk score and the analyzed data, and determining, based on the rating assigned to at least one of the one or more social entities that the at least one social entity is a security risk to the protected social entity.

Abstract: A computer-implemented method including scanning, by one or more processors, data that is maintained on one or more social networks, identifying, one or more social entities associated with the scanned data, determining a risk score for each of the one or more social entities, analyzing the scanned data using one or more machine learning techniques, assigning a rating to each of the one or more social entities based on the determined risk score and the analyzed data, and determining, based on the rating assigned to at least one of the one or more social entities that the at least one social entity is a security risk.