Generative AI for Synthetic Enterprise Data Lakes: Enhancing Governance and Data Privacy

The rapid adoption of enterprise data lakes has amplified concerns surrounding data governance, compliance, and privacy. Traditional governance models often struggle to balance accessibility with stringent regulatory requirements, leading to inefficiencies and compliance risks. This study explores the application of generative AI for constructing synthetic enterprise data lakes that preserve analytical utility while mitigating privacy exposure. By leveraging advanced generative modeling, the proposed framework enables the creation of high-fidelity synthetic datasets that mimic the statistical and relational properties of real enterprise data. This approach supports safe data sharing, reduces reliance on sensitive datasets, and enhances regulatory compliance. The paper further evaluates the governance implications of integrating synthetic data generation into enterprise architectures, highlighting improvements in auditability, policy enforcement, and data lifecycle management. Results demonstrate that generative AI–powered synthetic data lakes not only strengthen privacy-preserving analytics but also optimize governance frameworks, paving the way for secure, compliant, and innovation-friendly enterprise data ecosystems.