Agentic AI-Powered Data Quality Guardians for Regulated Industries

Manish Tomar (), Vasudevan Ananthakrishnan () and Muthuraman Saminathan ()

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2024, vol. 3, issue 1, 507-531

Abstract: In regulated industries such as healthcare, finance, and pharmaceuticals, ensuring data quality is not merely a matter of efficiency but of compliance, trust, and safety. This paper introduces the concept of Agentic AI-Powered Data Quality Guardians—autonomous, intelligent agents designed to proactively monitor, assess, and enhance data quality across complex and evolving systems. Leveraging advancements in agentic artificial intelligence (AI), these digital guardians operate with minimal human oversight, employing reasoning, learning, and self-correction to maintain data integrity in real time. The proposed framework combines rule-based validation, anomaly detection, semantic enrichment, and regulatory alignment to ensure compliance with stringent industry standards. Case studies and simulations demonstrate the effectiveness of these agents in improving data accuracy, completeness, and consistency, thereby reducing operational risk and audit failure. This research underscores the transformative potential of agentic AI in modernizing data governance and fostering a resilient, compliant data ecosystem in high-stakes sectors.

Keywords: Agentic AI; Data Quality; Regulated Industries; Autonomous Agents; Data Governance; Compliance; Data Integrity; Healthcare Data (search for similar items in EconPapers)
Date: 2024
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