AI Governance Scan Engines for Enterprise Data Ecosystems: A RAG‑Driven Architecture for Continuous Compliance and Risk Detection

Abstract

The reliance of enterprises on complex data ecosystems has grown, enabling them to power their artificial intelligence (AI), machine learning (ML), analytics and automated decision-making processes. Flaws in data quality, privacy concerns, biases, inconsistent data quality, and incomplete data lineage, as well as broken governance practices and changing compliance regulations can taint the trustworthiness and accountability of AI systems. To support on-going compliance assessment and enterprise risk detection, this paper introduces an AI Governance Scan Engine that utilizes Retrieval-Augmented Generation (RAG). The architecture proposed combines metadata catalogs, lineage graphs, policy repositories, vector stores, governance-centric large language models, automatic scan orchestration, enforcement, and reporting. The engine continuously scans for policy violations, fairness, privacy risks, lineage integrity, data quality and model input governance in enterprise data lakes, warehouses and machine-learning pipelines. A multi-dimensional risk scoring system that ties severity, likelihood, governance impact, and confidence to identify findings and take the appropriate remediation. The framework can accommodate scheduled, event-driven, and pipeline-integrated governance scans, and audit evidence is provided, along with the ability to have human oversight on high-impact decisions. The proposed approach establishes a scalable framework for proactive, explainable and ongoing AI governance by linking organizational policies to enterprise data context via RAG. Moves enterprise governance beyond the reactive audit to intelligent, automated and risk-aware monitoring of AI-driven data ecosystems.