Large Language Models for Intelligent Data Stewardship in Enterprises: Architectures, Provenance, and Evidence-Mapped Governance

Abstract

Enterprises increasingly operate in data ecosystems characterized by extreme heterogeneity, spanning legacy databases, cloud-native platforms, streaming pipelines, and unstructured knowledge repositories, all under mounting regulatory, ethical, and operational scrutiny. In this context, recent advances in large language models (LLMs) notably transformer-based architectures combined with retrieval-augmented generation (RAG), dense passage retrieval (DPR), programmatic and weak supervision, and knowledge-graph grounding offer a unifying technical substrate for intelligent data stewardship at enterprise scale. By embedding stewardship objectives such as FAIR principles, end-to-end provenance, semantic interoperability, and continuous data quality assurance directly into LLM-enabled workflows, organizations can move beyond static catalogs toward adaptive, context-aware systems capable of automated metadata enrichment, lineage-aware question answering, policy-sensitive data discovery, and assisted remediation of quality and compliance issues. This article synthesizes these foundations into an integrated reference architecture for LLM-assisted stewardship, and introduces an evidence-mapping methodology that operationalizes governance assessment by aligning publicly observable signals with established standards and controls. Through an applied case study of Inspire Brands’ AI-driven governance initiatives, we demonstrate how evidence mapping enables a non-invasive yet systematic evaluation of organizational readiness, surfacing both strengths and gaps without requiring privileged internal disclosures. Finally, we outline open research challenges including evaluation metrics for trustworthiness and explainability, robustness under regulatory change, and human-in-the-loop validation patterns and offer practical recommendations to guide enterprise adopters in responsibly deploying LLMs as first-class components of modern data governance and stewardship ecosystems.