Abstract: A system and method for collaborative smart evidence gathering and investigation for incident response attack surface management and forensics in a computing environment is disclosed. The system obtains evidence data from multiple sources with various entry points, capturing contextual information. Further, the system processes the data using an artificial intelligence (AI) root cause analysis, graph augmented retrieval, semantic classifier, meaning extraction, and causal discovery model. Furthermore, the system performs similarity analysis to assess evidence quality, sufficiency, and completeness. Based on the evaluation, the system determines appropriate actions to be taken on the processed evidence data. Additionally, the system executes the actions to resolve the incidents effectively by using a smart expert system, a human agent participation, or an AI co-pilot, as a first-class investigator and collaborator in the process.

Abstract: A computer-implemented system and method for generating time-based contextual graph in cloud computing environment is disclosed. The system receives, from computing devices, requests corresponding to security anomaly analysis of cloud infrastructure-based resources and applications. The system identifies parent node and node attributes corresponding to parent node in cloud infrastructure-based resources and/or applications, based on requests. Further, system determines hierarchical node relationships between parent node and child nodes, based on identified node attributes. Furthermore, system analyses risks in context of security anomaly associated with cloud infrastructure-based resources and/or applications, based on hierarchical node relationships between parent node and child nodes. Further, system generates time-based contextual graph corresponding to analyzed risks in context of security anomaly, based on determined hierarchical node relationships.