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.

翻译：在数字安全至关重要的时代，通过供应链安全问卷高效处理安全相关查询势在必行。本文提出一种利用自然语言处理（NLP）与检索增强生成（RAG）来自动化生成这些响应的新方法。我们开发了QuestSecure系统，该系统通过将大语言模型（LLM）与先进的检索系统相结合，能够解析多种文档格式并生成精确的响应。我们的实验表明，QuestSecure显著提升了响应准确性和操作效率。通过采用先进的NLP技术和定制的检索机制，该系统能够持续生成上下文相关且语义丰富的响应，从而减轻安全团队的认知负荷并最大程度减少潜在错误。这项研究为自动化复杂的安全管理任务、提升组织安全流程提供了有前景的途径。