Enterprise-Scale Privacy Engineering: A Unified Data-Centric Architecture for Masking, Synthetic Data, and Governance across Pre-Production Environments
DOI:
https://doi.org/10.15680/IJCTECE.2025.0801012Keywords:
Data-centric architecture, data masking, synthetic data, privacy governance, pre-production environments, enterprise privacy engineeringAbstract
The problem of ensuring the protection of sensitive data on the pre-production systems, including development, testing, staging, and quality assurance, is becoming more common in enterprises as more strict regulations are introduced, as well as the introduction of AI working loads and the active increase in the volume of data. Traditional methods tend to treat data masking, synthetic data generation, and governance separately, resulting in a discontinuous process, lopsided privacy assurance, extremely high re-identification risk, and a very slow environment setup. The given paper presents a system that integrates these three elements into one so that the data became the center of the system and enables to achieve masking of structured fidelity using methods like substitution, tokenization, and consistent hashing, synthetical generation of added volume, variety, and diversity using AI models like GANs, VAEs, and diffusion and having this architecture centrally managed, imposing uniform policy, tracing lineage, access control, privacy budgets, and automatic audit log with propagation of rules in real-time. The hybrid architecture can significantly reduce the privacy exposure, reduce the process of provisioning by an order of magnitude faster, store less storage in the form of on-demand virtual views as opposed to full, persistent copies, and offer a stable presence in the domains of secure testing, analytics, and large-scale AI development in highly regulated industries. The combined model provides a scalable, efficient approach to managing large volumes of sensitive data while meeting stringent privacy, compliance, and innovation demands.
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