A Privacy -First Governance Architecture for Compliant Generative AI Data Pipelines: Regulatory Guardrails and Data Protection Frameworks for Enterprise AI Deployments
DOI:
https://doi.org/10.15680/IJCTECE.2026.0901018Keywords:
Privacy-First Architecture, Data De-Identification, Synthetic Data Generation, HIPAA Compliance, Generative AI GovernanceAbstract
As huge volumes of sensitive data (PHI, PII, PCI, etc.) feed Gen AI training/fine-tuning, RAG, and inference pipelines, privacy risks to enterprise deployments continue to grow. It causes sensitive data to be memorized in models from prompt leakage/inversion. Shadow AI is still growing uncontrolled. Penalties can reach the millions for non-compliance (HIPAA, GDPR, CCPA/CPRA, etc.) and can be critical to brand value in regulated industries. How to adopt Gen AI at scale while ensuring privacy compliance? The privacy-first, governed architecture enables this by building discovery, de-identification, and validation in every pipeline. Features state-of-the-art masking, tokenization with referential integrity, synthetic data, and automated quality gates. Validated at enterprise scale across two regulated industries: a large U.S. managed care organization successfully de-identified 34TB of data across more than 1,100 tables and 1,200 sensitive fields using six production de-identification frameworks, enabling HIPAA-compliant GenAI access for 28 Agile teams; and a large U.S. multi-format retailer modernized its test data management platform, delivering privacy-compliant, AI-ready data pipelines for more than 50 Agile teams through an award-winning intelligent provisioning platform
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