RAG for Effective Supply Chain Security Questionnaire Automation
This work addresses the need for efficient security management in organizations, though it appears incremental in applying existing methods to a specific domain.
The paper tackled the problem of automating responses to supply chain security questionnaires by developing QuestSecure, a system using NLP and RAG, which significantly improved response accuracy and operational efficiency.
In an era where digital security is crucial, efficient processing of security-related inquiries through supply chain security questionnaires is imperative. This paper introduces a novel approach using Natural Language Processing (NLP) and Retrieval-Augmented Generation (RAG) to automate these responses. We developed QuestSecure, a system that interprets diverse document formats and generates precise responses by integrating large language models (LLMs) with an advanced retrieval system. Our experiments show that QuestSecure significantly improves response accuracy and operational efficiency. By employing advanced NLP techniques and tailored retrieval mechanisms, the system consistently produces contextually relevant and semantically rich responses, reducing cognitive load on security teams and minimizing potential errors. This research offers promising avenues for automating complex security management tasks, enhancing organizational security processes.