RAG for Effective Supply Chain Security Questionnaire Automation
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909433144016896 |
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| author | Reza, Zaynab Batool Syed, Abdul Rafay Iqbal, Omer Mensah, Ethel Liu, Qian Rahman, Maxx Richard Maass, Wolfgang |
| author_facet | Reza, Zaynab Batool Syed, Abdul Rafay Iqbal, Omer Mensah, Ethel Liu, Qian Rahman, Maxx Richard Maass, Wolfgang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_13988 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RAG for Effective Supply Chain Security Questionnaire Automation Reza, Zaynab Batool Syed, Abdul Rafay Iqbal, Omer Mensah, Ethel Liu, Qian Rahman, Maxx Richard Maass, Wolfgang Machine Learning 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. |
| title | RAG for Effective Supply Chain Security Questionnaire Automation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2412.13988 |