Abstract: Data quality awareness in security analytics solutions is provided. A system can include one or more processors, coupled to memory. The system can receive a data set comprising one or more events generated for interactions between a plurality of computing devices that execute applications over a network. The system can select a field in the one or more events of the data set that impacts an action performed with the data set based on a model trained via machine learning with a historical data set of events generated for interactions between computing devices. The system can determine a data quality score based on values of the field in the one or more events of the data set. The system can provide an indication of performance of the action with the data set based on a comparison of the data quality score with a threshold.

Abstract: Described embodiments provide systems and methods for automatic cluster scaling. A processor can receive a metric of a first metric type for a cluster of computing devices and an identification of a version of an application and. The processor can aggregate a metric of the first metric type for the version of the application and the metric for the cluster of computing devices to establish an third metric. The processor can determine the third metric exceeds a threshold for the first metric type for the cluster of computing devices, the threshold proportional to a capacity of the cluster of computing devices for the first metric type. The processor can increase a size of the cluster of computing devices.

Abstract: A method may include generating, by a computing system, a first tenant-specific model for a first tenant. The method may further include generating, by the computing system, first metrics for the first tenant-specific model. The method may also include generating, by the computing system, a second tenant-specific model for the first tenant. The method may additionally include generating, by the computing system, second metrics for the second tenant-specific model. Moreover, the method may include comparing, by the computing system, the first metrics and the second metrics to select one of the first tenant-specific model and the second tenant-specific model as a first selected tenant-specific model for the first tenant.