AI Driven Data Validation and Compliance Framework for Real-Time Cloud Pipelines

The current paper provides an artificial intelligence-based end to end data validation and regulatory compliance framework in real time cloud native data pipelines. Current solutions tend to apply data quality assurance and compliance enforcement separately or as an after-processing activity which causes a fragmented trust, delayed anomaly detection, and a lack of flexibility to changing regulations. To overcome such restrictions, the framework presented here proposes a cascaded structure that consists of two complementary modules: the Aegis Fuzzy Recta Net, which is responsible of the adaptive detection and rectification of anomalies and the Lexi-Regula Transformer Engine, which is needed to interpret and enforce regulatory policies dynamically. Raw streaming data are consumed, regularized and verified and then finally undergo compliance classification in a single operational cycle. The framework is tested on the AWS Public NYC Taxi and Limousine Trips data to reflect scalability and application in real time. The experimental results demonstrate effective reduction of the severity of the anomaly and the high performance in the compliance classification in terms of precision, recall, and overall accuracy. The suggested solution allows making interpretable, regulation-conscious decisions and being robust and scalable, which is appropriate in the context of compliance-sensitive distributed cloud deployments.