Compliance Strategies for Big Data Processing in the Cloud: A Focus on Data Protection Regulations

Abstract

This paper explores compliance strategies for big data processing in cloud environments, focusing on the challenges of adhering to evolving data protection regulations. The rapid proliferation of large-scale data repositories, fueled by advanced analytics and pervasive connectivity, poses significant obstacles to organizations seeking to balance utility, security, and legality. In particular, recent regulations have magnified the need for robust privacy protections, cryptographic techniques, and risk assessment models that address issues such as lawful cross-border data transfers and continuous audit compliance. To address these requirements, we investigate new architectures for cloud-based systems capable of dynamically enforcing region-specific constraints, developing techniques that formally capture legal contexts and policy translations within mathematical models of data flow. Our analysis prioritizes the practical implications of these theoretical considerations, highlighting how organizations can leverage computationally efficient algorithms and secure storage frameworks to align with legal mandates. We further examine the influence of distributed machine learning pipelines, in which individual components must integrate strict data use regulations without undermining key performance metrics. Potential impacts on enterprise resource management, infrastructure design, and multi-cloud orchestration strategies are also discussed. By synthesizing multiple approaches, we present a viable methodology for reconciling big data processing with pressing regulatory demands, ultimately facilitating enhanced privacy controls while preserving analytical power and operational scalability.