Data fabric architecture with machine learning (ML) provides the promise of transformations—but with many security issues. This study focuses on the most visible data security threats in a learning-driven data fabric such as adversarial attacks, privacy leaks, authentical threats, and encryption risks. This study delves into research by which adversarial approaches could fail and data integrity could be spoiled. This study also deals with authentication and role-based access control for insulated data nodes to prevent unauthorized guest access. This study examines encryption techniques with other emerging technologies to analyze their effectiveness in securing data. It elaborates on the functions AI and ML play in threat detection, predictive analytics, and adaptive modelling to augment agility at real-time defence levels. We also delve into the implications of quantum computing on encryption and why we need post-quantum cryptography strategies.