构建可自适应的生成式AI系统，提升网络安全防御的精准与鲁棒性
The Road of Adaptive AI for Precision in Cybersecurity
- 通过检索与模型双层级自适应机制，动态响应威胁知识变化
- 实证表明该架构显著降低误报率，提升威胁检测精度
- 适合安全工程师与生成式AI研发者参考落地实战方案
网络安全的持续复杂化为人工智能研究与实践带来独特挑战与机遇。本文分享了在生产环境中设计、构建与运维生成式AI流水线的关键经验，重点聚焦于应对不断演变的知识库、工具链和攻击威胁所需的持续自适应能力。目标是为人工智能从业者及产业相关方提供可操作的视角，尤其关注不同自适应机制在端到端系统中的互补作用。我们基于真实部署提出实用指导，总结了利用检索与模型级自适应的最佳实践，并指出现有研究中亟待突破的方向，以推动生成式AI在网络安全防御中实现更高鲁棒性、精确性与可审计性。
原文摘要 · Abstract (English)
Cybersecurity's evolving complexity presents unique challenges and opportunities for AI research and practice. This paper shares key lessons and insights from designing, building, and operating production-grade GenAI pipelines in cybersecurity, with a focus on the continual adaptation required to keep pace with ever-shifting knowledge bases, tooling, and threats. Our goal is to provide an actionable perspective for AI practitioners and industry stakeholders navigating the frontier of GenAI for cybersecurity, with particular attention to how different adaptation mechanisms complement each other in end-to-end systems. We present practical guidance derived from real-world deployments, propose best practices for leveraging retrieval- and model-level adaptation, and highlight open research directions for making GenAI more robust, precise, and auditable in cyber defense.
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