From Risk Principles to Runtime Defenses: Security and Governance Frameworks for Big Data in Finance

Financial institutions are simultaneously among the most data-intensive and the most heavily regulated industries. The rise of big data platforms, distributed file systems, event-driven ingestion backbones, and advanced analytics engines has created extraordinary opportunities for fraud detection, customer insight, and regulatory reporting. Yet the same platforms magnify risks around privacy, security, and governance. This article reviews the evolution of security and governance frameworks for big data in regulated finance from 2000 through early 2018. Drawing on international regulations (Basel III, PCI DSS v3.2, GDPR, FFIEC, FCA FG16/5), industry best practices (NIST SP 800-53, NIST Big Data Interoperability Framework), and emerging open-source governance tools (Apache Ranger, Apache Atlas), we propose a layered control architecture. Three illustrative figures—the NIST Big Data Reference Architecture, its security/privacy overlay, and the container security lifecycle—demonstrate how regulated financial institutions can align technical implementations with supervisory mandates