用AI自动回答供应链安全问卷，准确高效
RAG for Effective Supply Chain Security Questionnaire Automation
- 结合大模型与检索增强生成，解析多格式文档
- 响应准确率和效率显著提升，减少人为错误
- 适合安全团队处理复杂问卷，降低认知负担
在数字安全至关重要的时代，高效处理供应链安全问卷中的安全问询至关重要。本文提出QuestSecure系统，利用自然语言处理（NLP）与检索增强生成（RAG）技术实现自动化响应。该系统能解析多种文档格式，并通过大型语言模型（LLMs）与先进检索机制的融合，生成上下文相关且语义丰富的精准回复。实验表明，该系统显著提升了响应准确率与运营效率，减轻安全团队的认知负荷，降低潜在错误风险。本研究为自动化复杂安全管理工作提供了可行路径，有助于优化组织安全流程。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。