Data Privacy Engineering in Cloud-Native Environments: Integrating DevPrivOps, Risk Modeling, and Privacy-Enhancing Technologies

Srinivasa Rao Seetala

International Journal of Scientific Research in Science and Technology, 2024, vol. 11, issue 5, 851-863

Abstract: The rapid adoption of cloud-native architectures has fundamentally transformed how modern applications are designed, developed, deployed, and scaled, enabling unprecedented levels of agility, elasticity, and resilience. However, this paradigm shift has simultaneously introduced complex challenges in maintaining data privacy, ensuring regulatory compliance, and managing secure data lifecycles across highly dynamic, distributed, and containerized environments. Traditional privacy mechanisms, which were designed for static and centralized systems, are increasingly inadequate in addressing the ephemeral nature of microservices, the proliferation of APIs, and the continuous integration and deployment (CI/CD) pipelines that characterize cloud-native ecosystems. In response, the field of data privacy engineering has emerged as a critical discipline that integrates privacy-by-design principles directly into software engineering processes. This paper explores this evolving domain with a particular focus on DevPrivOps, an extension of DevOps that embeds privacy controls, automated compliance checks, and continuous monitoring into development workflows. It further examines key components such as privacy risk modeling for proactive threat identification, data flow tracking for visibility and accountability, and the adoption of privacy-enhancing technologies (PETs) including differential privacy, homomorphic encryption, and secure multi-party computation. Additionally, the paper discusses how organizations can operationalize scalable and automated privacy controls through policy-as-code, data classification frameworks, and runtime enforcement mechanisms. By synthesizing insights from recent research studies and established theoretical foundations, this work proposes a structured and practical approach to embedding robust privacy engineering practices within cloud-native software development, ultimately enabling organizations to balance innovation with trust, security, and regulatory adherence.

Keywords: Cloud-native computing; Data privacy engineering; DevPrivOps; Privacy-by-design; GDPR; Data governance; Differential privacy; Secure data pipelines; Privacy risk assessment; Cloud security (search for similar items in EconPapers)
Date: 2024
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DOI: 10.32628/IJSRST52310282

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