<![CDATA[Cloud environments are now central to organizational operations and hold sensitive information and essential business processes that depend on trust between users and the systems they access. Insider threats present a significant challenge in this setting because individuals with legitimate access understand how these environments operate and can misuse their privileges. Traditional insider threat mitigation approaches are mostly reactive. They often rely on delayed evidence collection and post-incident investigation, which results in incomplete records, late detection, and increased organizational harm. This highlights the need for proactive strategies that identify suspicious behavior early and support reliable forensic investigation. This study addresses the lack of a clear Digital Forensic Readiness framework that can manage insider threats in cloud environments. It introduces a readiness model that integrates forensic principles with intelligent behavioral analytics to detect, interpret, and preserve indicators of insider activity in Software as a Service environments. The research includes a detailed review of existing literature, identifies gaps in insider threat mitigation, and presents a practical scenario that illustrates how the framework supports investigation. In addition, the study proposes a structured approach for extracting and preparing data to improve anomaly detection and timely threat recognition. The framework aligns with ISO/IEC 27043 standards by promoting modularity, scalability, and evidential reliability. This work contributes a proactive and forensically sound approach to insider threat detection and establishes a foundation for future validation and adoption across organizations.]]>
