Abstract: In response to the challenges faced by traditional cybersecurity detection and assessment technologies—such as large system scales, dynamic supply chain risks, and insufficient evaluation depth—this paper explores the application of AI technologie to advance this field. Methodologically, an endtoend implementation framework for largescale models is proposed, consisting of “data preparationdistillation and annotationcluster trainingquantitative deployment.” A localized compliance assessment model based on retrievalaugmented generation (RAG) technology is developed, and a multimodal model supporting joint textimage analysis is deployed. The large model significantly shortens the assessment cycle in scenarios such as provincial government clouds, improves the efficiency of compliance knowledge matching while reducing computational load by 70%, and markedly enhances the detection rate of inherent defects. The conclusion indicates that AI technology can effectively overcome the limitations of traditional assessment methods, promoting cybersecurity detection and assessment toward greater intelligence, adaptability, and comprehensiveness, thereby providing support for building resilient cybersecurity protection systems and fostering related ecosystem development.

Key words: cybersecurity, detection and assessment, security compliance, large language model (LLM)

摘要： 针对传统网络安全检测评估面临系统规模庞大、供应链风险动态变化、测评深度不足的挑战，研究AI技术赋能该领域的应用.从方法上，提出“数据准备—蒸馏标注—集群训练—量化部署”全流程大模型落地路径，构建基于检索增强生成(retrievalaugmented generation, RAG)技术的本地化合规测评大模型，部署支持文本与图像联合分析的多模态大模型.大模型将省级政务云等场景测评周期大幅压缩，合规知识匹配效率提升且计算量减少70%，内生缺陷检出率显著提高.结论表明，AI技术可有效突破传统测评局限，推动网络安全检测评估向智能化、自适应、全态化演进，为构建韧性网络安全防护体系及相关生态发展提供支撑.

关键词: 网络安全, 检测评估, 安全合规, 大模型, AI技术