Abstract: A cyberrisk governance method to automate cybersecurity detection and resolution in a network is disclosed. The method includes monitoring data related to one or more cybersecurity, Information Technology (IT) operation responses, and governance controls to address regulation, compliance and enterprise risk. Further, the method includes detecting one or more threats in the data using Machine Learning (ML). The one or more threats correspond to at least a malware detection, an intrusion detection, fraud detection for IT, and user behavioral analysis for the internet of things (IoT). The method further includes generating a report for a user based at least on the detected one or more threats. The generated report includes one or more security information and one or more security patches. Thereafter, the method includes sending the generated report to the user, thereby automating cybersecurity detection and resolution in the network.

Abstract: A cyberrisk governance method to automate cybersecurity detection and resolution in a network is disclosed. The method includes monitoring data related to one or more cybersecurity, Information Technology (IT) operation responses, and governance controls to address regulation, compliance and enterprise risk. Further, the method includes detecting one or more threats in the data using Machine Learning (ML). The one or more threats correspond to at least a malware detection, an intrusion detection, fraud detection for IT, and user behavioral analysis for the internet of things (IoT). The method further includes generating a report for a user based at least on the detected one or more threats. The generated report includes one or more security information and one or more security patches. Thereafter, the method includes sending the generated report to the user, thereby automating cybersecurity detection and resolution in the network.