A method, comprising: receiving, by a device, historical data associated with multiple cloud computing environments, wherein the historical data includes data identifying usage of resources of the multiple cloud computing environments by organizations; determining a multi-entity profile for the historical data, wherein the multi-entity profile includes data groupings of the historical data based on a set of attributes included in the historical data, wherein the set of attributes includes one or more of: an attribute identifying types of resources of the cloud computing environments, an attribute identifying tasks for which the resources are organized, or an attribute identifying users of the resources; identifying trends and patterns in the historical data based on the data groupings of the multi-entity profile; training, by the device, multiple machine learning models, with the historical data and data identifying the trends and the patterns, to generate multiple trained machine learning models, wherein the training of the multiple machine learning models generates outputs, and wherein the outputs include anomaly scores indicating anomalies related to resource usage by organizations; training, by the device, a super model with the outputs to generate a trained super model; receiving, by the device, particular data associated with one of the multiple cloud computing environments, wherein the particular data at least includes data identifying usage of resources associated with the cloud computing environment, and wherein the resources include computing resources, processing resources, memory resources, and/or network resources; processing, by the device, the particular data, with the multiple trained machine learning models, to generate one or more anomaly scores indicating anomalous usage of the resources associated with the cloud computing environment; processing, by the device, the anomaly scores, with the trained super model, to generate a final anomaly score indicating anomalous usage of at least one of the resources associated with the cloud computing environment; and performing, by the device, one or more actions based on the final anomaly score. receiving, by a device, historical data associated with multiple cloud computing environments, wherein the historical data includes data identifying usage of resources of the multiple cloud computing environments by organizations; determining a multi-entity profile for the historical data, wherein the multi-entity profile includes data groupings of the historical data based on a set of attributes included in the historical data, wherein the set of attributes includes one or more of: an attribute identifying types of resources of the cloud computing environments, an attribute identifying tasks for which the resources are organized, or an attribute identifying users of the resources; an attribute identifying types of resources of the cloud computing environments, an attribute identifying tasks for which the resources are organized, or an attribute identifying users of the resources; identifying trends and patterns in the historical data based on the data groupings of the multi-entity profile; training, by the device, multiple machine learning models, with the historical data and data identifying the trends and the patterns, to generate multiple trained machine learning models, wherein the training of the multiple machine learning models generates outputs, and wherein the outputs include anomaly scores indicating anomalies related to resource usage by organizations; wherein the training of the multiple machine learning models generates outputs, and wherein the outputs include anomaly scores indicating anomalies related to resource usage by organizations; training, by the device, a super model with the outputs to generate a trained super model; receiving, by the device, particular data associated with one of the multiple cloud computing environments, wherein the particular data at least includes data identifying usage of resources associated with the cloud computing environment, and wherein the resources include computing resources, processing resources, memory resources, and/or network resources; wherein the particular data at least includes data identifying usage of resources associated with the cloud computing environment, and wherein the resources include computing resources, processing resources, memory resources, and/or network resources; processing, by the device, the particular data, with the multiple trained machine learning models, to generate one or more anomaly scores indicating anomalous usage of the resources associated with the cloud computing environment; processing, by the device, the anomaly scores, with the trained super model, to generate a final anomaly score indicating anomalous usage of at least one of the resources associated with the cloud computing environment; and performing, by the device, one or more actions based on the final anomaly score.