Building Compliant Data Pipelines in Regulated Sectors: A Privacy-First Engineering Approach - Iboro Akpan Essien,; Emmanuel Cadet,; Joshua Oluwagbenga Ajayi,; Eseoghene Daniel Erigh,; Ehimah Obuse,; Noah Ayanbode,; Lawal Abdulmutalib Babatunde, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024)
As data becomes increasingly central to operations in regulated sectors such as healthcare, finance, and government, the imperative for privacy-first data engineering has never been more urgent. This presents a comprehensive exploration of how compliant data pipelines can be architected and operationalized with privacy at their core, addressing the mounting complexity of regulatory landscapes defined by frameworks such as the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), and the California Consumer Privacy Act (CCPA). We argue that traditional data pipelines—built for scale and speed—must evolve into systems that prioritize legal compliance, data protection, and ethical handling of sensitive information. Central to this transformation is the application of Privacy-by-Design principles, which advocate embedding data minimization, access control, encryption, and purpose limitation directly into the architecture of data systems. This outlines how consent-aware ingestion, anonymization during transformation, and policy-based access control in storage and output stages can help ensure compliance without sacrificing analytical capabilities. This also reviews contemporary tools and frameworks that enable compliance automation, including policy-as-code engines like Open Policy Agent (OPA), metadata governance platforms like DataHub and Amundsen, and audit-enabling observability stacks. Through case studies in healthcare and finance, we illustrate how privacy-first pipelines are implemented in real-world environments to achieve both regulatory adherence and business agility. This concludes by examining emerging frontiers, such as AI-powered compliance, federated data processing, and privacy-enhancing technologies (PETs), emphasizing the strategic importance of proactive and scalable compliance architectures. We advocate for a cultural and technical shift where privacy is not an afterthought but a foundational design requirement—transforming compliance from a bottleneck into a competitive advantage in data-driven innovation.
Keywords: Building compliant; Data pipelines; Regulated sectors; Privacy; Engineering approach