Data analytics using LLMs without compromising on data security and privacy

Abstract

Large Language Models (LLMs) are evolving rapidly, and their adoption across various software domains continues to accelerate. One of the areas where LLMs have demonstrated significant value is data analytics, enabling users without specialized SQL knowledge to interact with and analyze data more effectively. Many modern analytics platforms leverage LLMs to generate insights quickly by transmitting datasets to the model along with user queries. The LLM then analyzes the data and attempts to provide meaningful responses. However, this approach presents several limitations. LLMs often struggle with quantitative tasks that require precise computations, and transmitting sensitive data to external models raises serious concerns regarding privacy, security, and regulatory compliance. In highly sensitive domains such as healthcare, finance, and government, sending raw data to LLMs may not be a viable option. This paper proposes an innovative architecture called SCQ (Structure, Context, and Question), which minimizes the need to transmit actual data while improving the quality and reliability of analytical insights. Instead of sending complete datasets, the proposed approach provides the LLM with the data structure, comprehensive contextual information describing the meaning of each data element, and the user's analytical question. By separating data from context, the framework enhances privacy, reduces security risks, and improves the accuracy of generated responses. The paper concludes with a practical case study demonstrating the applicability of the SCQ framework across a wide range of real-world data analytics scenarios.