The integration of cloud computing and artificial intelligence (AI) requires advanced data fabric architectures to manage heterogeneous datasets and enable privacy-preserving analytics across multiple domains. This paper introduces a data fabric framework creation proposal for a generic Cloud- AI solution which has multi-domain application as banking, healthcare and utilities through the employment of Variational Autoencoders (VAEs) with differential privacy for synthetic data generation. A comprehensive and well-structured preprocessing pipeline that deals with cross domain data misalignment, achieves 94%–96% anomaly detection accuracy, and enhances scalability by a 60% increase of the data volume (304,807 to 487,691 samples) standardizes column names and structures. The privacy-utility trade-off has been evaluated through AUPRC (0.0649–0.0806 for synthetic data), F1-score (0.05–0.06 of Frad class), KL-divergence \(({<}0.02)\) supported by illustrative data such as precision recall curves, distribution of data points, privacy-utility trade-off, and aiming for the value of KL-divergence to convince visualizations. The framework achieved 81.58% of motivated samples through 182,884 synthetic data generation at a 30% operational minimum cost, generated data and increased processing efficiency with confirmed class imbalance and challenges that the solution has been tailored to an enterprise level scalability, Cloud- AI solution with validated pseudonymization methods, moving away from k-anonymization.