Multi-Layer Trust Frameworks For LLM-Powered Data Enrichment In Enterprise Analytics Pipelines Akhil Kumar Kandakatla

Abstract

Automated data enrichment powered by large language models has emerged as a consequential capability in enterprise data platform engineering, enabling organizations to augment structured business records with AI-generated contextual outputs at scale (Brown et al., 2020; McKinsey Global Institute, 2023). While the technical challenges have been solved‚ the remaining challenge is deciding when an AI-enrichment is reliable enough to be used for downstream applications․ A common guardrail is confidence scoring․ However‚ it cannot detect systematic bias‚ quality drifts across target populations‚ or governance failures that can result in organizations losing trust in the data assets produced by LLMs (Guo et al․‚ 2017; Xiong et al․‚ 2024)․ This article proposes a four-layer trust framework for enterprise LLM enrichment governance, spanning output-level reliability assessment, organizational validation and phased adoption, governance and accountability, and continuous observability. An organizational maturity model accompanies the framework to enable enterprises to assess and advance their governance posture across four defined levels. Together, the framework and maturity model address a documented gap between technical uncertainty quantification and the institutional mechanisms through which organizations develop and sustain trust in AI-generated data. Embedding this governance architecture at the data platform design stage, rather than applying it as post-deployment remediation, produces substantially improved risk management and downstream decision quality outcomes.