Governed LLM Interfaces for Enterprise Data Access: Chat-Based Querying with Policy Constraints

Abstract

Large Language Models (LLMs) are turning into conversational interfaces for accessing the data of the organization while the enterprises work to make data more accessible across roles and departments. Chat-based LLM systems facilitate natural language conversations that open data access to all, eliminate reliance on technical staff, and speed up decision-making. Still, it also brings about serious issues like data security, regulatory compliance, and internal governance. The problems are especially acute if the information is sensitive or is in a silo. Enterprises are going to have to deal with risks resulting from unregulated queries, potential leakage of data, and the violation of access rights. Our research, to solve this problem, investigates the concept of a governed interface model, where LLMs are wrapped with policy-aware restrictions that provide the implementation of data usage rules, user permissions, and compliance boundaries in real time. The system not only directly incorporates access policies and governance logic into the LLM pipeline, but also allows secure, context-aware conversations with enterprise data. As a case in point, we illustrate a financial services firm that rolled out an actively regulated LLM interface that allowed the business users to access customer analytics and operational metrics freely. The implementation did not only keep the strict role-based access controls and the ability to be audited, but it also maintained the flexibility and usability of a conversation-style interface. The key results show that there have been significant gains in adoption and productivity while there was a drastic drop in the number of unauthorized query attempts. The case also points out some practical challenges in policy enforcement, latency management, and user training. Broadly speaking, the study demonstrates the capability of LLMs to serve as enterprise-grade data interfaces when combined with the governance schemes that ensure that the flow of users is controlled while still maintaining access. This approach stirs up a practical pathway from here on for organizations that want to safely draw upon the power of AI-driven, chat-based data access.