Abstract: A method for automatic management of user permissions in an organization including automatically grouping users into a plurality of user clusters based on at least one similarity between users in each user cluster, for each user cluster, automatically generating a set of cluster user permissions, the set of cluster user permissions including user permissions belonging to users in the cluster and actively used by at least one user in the cluster and for each user cluster, automatically modifying user permissions of each user in each cluster in accordance with the set of cluster user permissions.

Abstract: A method for automatic management of user permissions in an organization including automatically grouping users into a plurality of user clusters based on at least one similarity between users in each user cluster, for each user cluster, automatically generating a set of cluster user permissions, the set of cluster user permissions including user permissions belonging to users in the cluster and actively used by at least one user in the cluster and for each user cluster, automatically modifying user permissions of each user in each cluster in accordance with the set of cluster user permissions.

Abstract: A method of detecting and handling anomalies in a network, including, collecting meta-data related to an environment of each organization using the network; wherein each organization deploys one or more computers connected to the network, extracting features from the meta-data, clustering organizations having common features into segments, collecting training data from all organizations, grouping the training data according to the segments, training a model for each segment with event data to detect and handle anomalies, analyzing event data of a segment with a respective model for that segment, providing a decision score responsive to the analyzing; and handling the anomaly based on the decision score.

Abstract: A method of detecting and handling anomalies in a network, including, collecting meta-data related to an environment of each organization using the network; wherein each organization deploys one or more computers connected to the network, extracting features from the meta-data, clustering organizations having common features into segments, collecting training data from all organizations, grouping the training data according to the segments, training a model for each segment with event data to detect and handle anomalies, analyzing event data of a segment with a respective model for that segment, providing a decision score responsive to the analyzing; and handling the anomaly based on the decision score.