A Unified AI System For Data Quality Control and DataOps Management in Regulated Environments

TL;DR

This paper introduces a unified AI-driven framework for data quality control and DataOps management in regulated environments, integrating multiple QC methods into a continuous, governed system to enhance data integrity, auditability, and compliance.

Contribution

It presents a novel integrated architecture combining rule-based, statistical, and AI methods for data QC within a continuous DataOps framework, specifically designed for regulated sectors.

Findings

Improved anomaly detection recall in financial data pipelines.

Reduced manual effort in data remediation processes.

Enhanced auditability and traceability in high-throughput workflows.

Abstract

In regulated domains such as finance, the integrity and governance of data pipelines are critical - yet existing systems treat data quality control (QC) as an isolated preprocessing step rather than a first-class system component. We present a unified AI-driven Data QC and DataOps Management framework that embeds rule-based, statistical, and AI-based QC methods into a continuous, governed layer spanning ingestion, model pipelines, and downstream applications. Our architecture integrates open-source tools with custom modules for profiling, audit logging, breach handling, configuration-driven policies, and dynamic remediation. We demonstrate deployment in a production-grade financial setup: handling streaming and tabular data across multiple asset classes and transaction streams, with configurable thresholds, cloud-native storage interfaces, and automated alerts. We show empirical gains in anomaly detection recall, reduction of manual remediation effort, and improved auditability and traceability in high-throughput data workflows. By treating QC as a system concern rather than an afterthought, our framework provides a foundation for trustworthy, scalable, and compliant AI pipelines in regulated environments.