Uncertainty Quantification and Data Provenance for Data Pipeline Security Analysis

Ensuring data integrity and reliability is essential for real-world applications, especially in automated decision-making and anomaly detection systems. In this study, we introduce a data pipeline augmentation tool that combines Uncertainty Quantification (UQ) techniques with Data Provenance Tracking to detect anomalies and shifts. By leveraging a task runner for pipeline orchestration, our approach ensures scalable, fault-tolerant execution while maintaining full traceability and monitoring at each processing stage.