Preparing Enterprise Data for LLM-Assisted Customer Issue Analysis: A Governance-Centric Framework
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I3P119Keywords:
Large Language Models (LLMs), LLM-Assisted Analytics, Data Governance, Privacy-Aware AI, Data Lineage, AI Explainability, Root Cause Inference, Enterprise AI Governance, Compliance FrameworksAbstract
The increasing adoption of Large Language Models (LLMs) in enterprise environments has transformed customer support operations by enabling intelligent issue classification, automated response generation, and context-aware analytics. The effectiveness performance of LLM-powered customer issue analysis relies on the quality, governance, security, and compliance of enterprise data preparation pipelines, though. Organizations are still grappling with a host of issues, including poor record management, differing metadata, concerns about privacy, and compliance with regulations, that hinder the trustworthiness and scalability of AI-powered customer service. To address these gaps, this study introduces a framework centered on governance principles for preparing data for use by LLMs to analyses customer issues, incorporating all of the following aspects into a single analytical architecture data ingestion, cleansing, metadata management, compliance enforcement, data lineage tracking and privacy-aware data preprocessing. This framework also adopts governance-centric components, like access control, audit logging, anonymization, semantic enrichment and policy validation, to enable secure and explainable AI operations. The analytical performance has been shown to be improved by experimental evaluation over 10,000 enterprise customer support tickets, resulting in 92.3% classification accuracy and 0.91 F1-score in comparison to conventional ungoverned LLM approaches. The framework also lowered the chances of hallucination, boosted readiness for compliance and kept the inference latency low enough to meet the needs of real-time enterprise applications. The findings show that data preparation with governance awareness significantly contributes to the reliability, transparency, and scalability of LLM-powered customer service solutions. The suggested framework offers a pragmatic approach for the trustworthy enterprise AI adoption and secure, compliant, and efficient analysis of customer issues for modern digital organizations.
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