International Journal of Science, Engineering and TechnologyAuthors: Professor James Harrison, Professor William Parker, Professor Steven Turner, Chaitanya Srinivas, Yashwanth kumar
Abstract: Cloud-native environments have transformed enterprise application development and data processing by enabling scalable, distributed, and highly automated architectures. However, the dynamic nature of containers, microservices, serverless platforms, APIs, and multi-cloud deployments introduces significant challenges for data privacy, regulatory compliance, and risk management. This research proposes a Risk-Aware Framework for Data Privacy Engineering and Compliance Management in Cloud-Native Systems that integrates privacy-by-design principles, automated risk assessment, regulatory controls, data classification, continuous compliance monitoring, and evidence-based governance. The framework establishes a structured approach for identifying sensitive data, assessing privacy risks across cloud-native components, mapping regulatory requirements to technical and organizational controls, and continuously collecting compliance evidence. It incorporates automated policy enforcement, identity and access management, encryption, data minimization, consent management, data lineage, audit logging, and privacy risk scoring to strengthen organizational privacy protection. An evidence-mapping mechanism connects regulatory obligations with implementation controls, monitoring activities, and audit artifacts, thereby improving compliance transparency and accountability. The proposed framework also supports continuous assessment of privacy risks throughout the software development and cloud operations lifecycle rather than relying solely on periodic compliance audits. The research demonstrates how risk-aware automation can reduce compliance gaps, improve visibility into sensitive data processing, strengthen privacy governance, and support faster adaptation to evolving regulatory requirements. The framework provides a practical foundation for organizations seeking to establish secure, scalable, and continuously compliant cloud-native data environments.
