CYBER THREAT MITIGATION THROUGH AI-ENABLED BIG DATA ANALYSIS IN CLOUD ADMINISTRATION

Abstract

A data-driven threat model (d-TM) for cloud-based ecosystems is presented in this study, with a focus on a thorough method of detecting and evaluating risks at every stage of data processing. Threat layers, actors, and a shared knowledge base are all included in the d-TM framework, which offers an organized threat analysis procedure that is assessed using a case study. The method emphasizes how important data is to corporate operations and how important it is to evaluate security risks in a comprehensive way when moving to cloud environments. risks are examined at every layer—agent, network, compute, application, and storage—by using a tier-based paradigm, which guarantees that risks are examined regardless of where the data is stored. Additionally, the model uses extensive knowledge bases including MITRE CWE, CAPEC, and NIST SP 800-53 for threat analysis and detects various threat actors, such as business users, operators, systems, and malicious actors. The steps of threat detection, mitigation, data collecting, and analysis make up the threat analysis process, which offers a methodical way to handle security risks related to cloud computing.