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Building AI Pipelines That Comply with GDPR, HIPAA, and Industry Standards

Sai Kalyani Rachapalli

ETL Developer

rsaikalyani@gmail.com

Abstract- The high take-up rate of artificial intelligence (AI) by different sectors has triggered critical interest in data security, privacy, and ethics compliance. This work discusses the design and process blueprint for the establishment of AI pipelines that comply with the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and different sectoral standards. The aim is to offer a sound methodology to incorporate compliance checks at every stage of the AI lifecycle, from data collection to deployment. Our method focuses on incorporating privacy-preserving methods, secure data engineering principles, and auditability. Through a comparative analysis of AI development processes and regulatory requirements, we identify friction points and suggest technical as well as organizational solutions. The paper also analyzes case studies to test the proposed framework and addresses its implication for AI governance and ethical AI deployment.

The research highlights the need for cross-disciplinary cooperation between legal professionals, data scientists, and organizational stakeholders to provide end-to-end compliance. We propose certain tools and technologies, including federated learning and secure multiparty computation, that enable the enforcement of regulatory requirements without undermining model effectiveness. This paper makes a contribution to current discussion by offering a guidebook to action for businesses to enact ethical AI while preserving competitive edge in a highly regulated digital environment.

Keywords- AI pipeline, GDPR, HIPAA, data privacy, compliance, ethical AI, data governance, security standards, machine learning, privacy-preserving computation

DOI: 10.55041/ISJEM00141