关键词: 网络安全, 检测评估, 安全合规, 大模型, AI技术

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)