迈向基于大语言模型的自动化安全配置方法的开发：符合乌克兰网络安全法规
Towards the Development of an LLM-Based Methodology for Automated Security Profiling in Compliance with Ukrainian Cybersecurity Regulations
AI总结：
本文探讨将国际最佳实践整合到国家监管体系中，提出利用大语言模型实现自动化安全配置的方法，以应对高密度混合威胁环境下的网络安全挑战。
摘要：
近年来，信息技术在各领域的快速发展迫使网络安全专家不断审查现有流程以防止未经授权访问机密信息。以乌克兰为例，本文探讨将ISO/IEC 27001和NIST网络安全框架整合到国家监管体系中的方法。重点放在从传统合规模型向基于风险的方法转变，通过最近采用的乌克兰规范文件进行示例。此外，本文提出利用增强型检索增强生成（RAG）的大语言模型（LLM）自动化开发目标安全配置的方法。通过整合国家法规和组织政策的向量数据库，所提出的RAG顾问减少了手动复杂性，最小化人为错误，并确保技术控制与法律要求的一致性。本研究通过提供一个结构化的工作流程，为高密度混合威胁环境中的AI辅助网络安全管理做出了贡献。
英文摘要：
In recent years, the pace of development of information technology in various areas has increased drastically, forcing cybersecurity specialists to constantly review existing processes in order to prevent unauthorized access to confidential information. Using Ukraine as a primary case study, this paper explores the integration of international best practices, specifically ISO/IEC 27001 and the NIST Cybersecurity Framework, into national regulatory systems. A focus is placed on the transition from traditional compliance models to risk-based approaches, exemplified by the recent adoption of the Ukrainian normative documents. Furthermore, we propose a methodology for automating the development of target security profiles using Large Language Models (LLMs) enhanced by RetrievalAugmented Generation (RAG). By integrating a vector database of national regulations and organizational policies, the proposed RAG-based advisor reduces manual complexity, minimizes human error, and ensures alignment between technical controls and legal requirements. This study contributes to the field by providing a structured workflow for AI-assisted cybersecurity management in environments characterized by high-intensity hybrid threats.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
