Abstract

This study offers a self-learning AI-powered framework for data transformation and validation automation within Oracle APEX applications, focusing on security and adaptability. Data workflows in enterprises are inflexible, sclerotic, and hard to scale on heterogeneous data schemas. To resolve these issues, the proposed framework incorporates lightweight self-learning AI models that automatically determine transformation and multi-layer validation rules. The architecture embeds these models into APEX processes, which allows for real-time schema-aware reasoning, complete audit trail logging, and secure multi-context enforcement. Within various live Oracle APEX modules such as HR, finance, and CRM, the framework was evaluated and achieved greater than ninety percent improvement on numerous benchmarks including transformation accuracy, validation precision, and manual correction workload. These outcomes support the model's capacity to generalize over diverse datasets from the enterprise and cross-system security boundaries, providing a flexible solution for ensuring persistent data integrity within advanced modern enterprise applications.