RAG for Effective Supply Chain Security Questionnaire Automation
Zaynab Batool Reza, Abdul Rafay Syed, Omer Iqbal, Ethel Mensah, Qian, Liu, Maxx Richard Rahman, Wolfgang Maass

TL;DR
This paper presents QuestSecure, an NLP and RAG-based system that automates supply chain security questionnaires, significantly improving response accuracy and efficiency in security management tasks.
Contribution
It introduces a novel RAG-based approach with a tailored retrieval system for automating security questionnaire responses using LLMs.
Findings
QuestSecure improves response accuracy
It enhances operational efficiency
Reduces cognitive load on security teams
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNetwork Security and Intrusion Detection · Artificial Intelligence in Healthcare · Customer churn and segmentation
