Data Lineage and AI in Insider Risk Management

This whitepaper discusses the challenges posed by the fragmentation of data in modern organizations and the inadequacy of traditional data loss prevention (DLP) and insider risk tools to address these issues. It outlines how data now moves fluidly across various platforms, complicating the security landscape. The paper highlights that over 80% of critical data exfiltrated by employees consists of fragmented or derivative data, which legacy tools fail to detect. It emphasizes the importance of data lineage and artificial intelligence (AI) in providing visibility into data movement and context around sensitive information. By mapping the life cycle of data objects, organizations can better understand how data is created, transformed, and shared. The document also presents a new model for data security that integrates lineage-based visibility and AI-driven insights, enabling security teams to respond effectively to insider risks. The conclusion offers practical steps for implementing a lineage-based security strategy within organizations.