Compliance Monitoring and Enforcement Through Log Analysis Using Large Language Models

Abstract

In the ever-expanding landscape of data-driven enterprises, the challenge of ensuring compliance with security standards has become paramount. This research project addresses this challenge through the development of an advanced Compliance Monitoring and Enforcement system utilizing Large Language Models (LLMs). The objective is to revolutionize the traditional approach to compliance monitoring, transitioning from data collection to meaningful analysis. Amidst the intricate logs generated in complex systems like that of Flipkart, the need for a comprehensive solution is evident. The proposed system aims to employ LLMs, such as GPT-2, to analyze logs, identify non-compliance, and provide actionable insights for remediation.

The increasing volumes of data and intricate systems in contemporary business environments necessitate a paradigm shift in compliance monitoring. Traditional methods often falter in efficiently categorizing and analyzing the overwhelming amount of log data. Leveraging LLMs, with their ability to understand context and semantics, promises to bring about a transformative change in the compliance monitoring landscape. The application of this research extends beyond Flipkart, impacting various industries such as finance, healthcare, telecommunications, and government agencies, ensuring a scalable and adaptable solution.