用自主智能体重构网络安全，让系统能自适应抗攻击、自动恢复。
Agentic AI for Cyber Resilience: A New Security Paradigm and Its System-Theoretic Foundations
- 构建基于智能体的自适应安全框架，实现感知-推理-行动闭环。
- 通过博弈论设计攻防平衡，使系统在攻击下仍能维持关键功能。
- 适合研究智能安全、自动化防御的工程师和安全架构师。
网络安全正因基础模型驱动的人工智能而深刻变革。大语言模型实现了大规模的自主规划、工具协同与战略适应，挑战了依赖静态规则、边界防御和人工流程的安全架构。本文主张从以预防为中心的安全范式转向以韧性为核心的新型安全模式。弹性系统需具备预见中断、攻击中维持关键功能、高效恢复及持续学习的能力。文章将此转变置于网络安全范式的演化脉络中，提出一个由自主智能体参与感知、推理、行动与适应的AI增强型安全范式，涵盖网络与网络物理系统。构建了系统级智能体工作流设计框架，提出通用智能体架构，并将攻防过程视为耦合的自适应过程；证明博弈论可统一指导自主性分配、信息流设计与时间结构组合。案例研究展示在自动化渗透测试、修复与网络欺骗中的应用，表明基于均衡的设计可实现系统级韧性。
原文摘要 · Abstract (English)
Cybersecurity is being fundamentally reshaped by foundation-model-based artificial intelligence. Large language models now enable autonomous planning, tool orchestration, and strategic adaptation at scale, challenging security architectures built on static rules, perimeter defenses, and human-centered workflows. This chapter argues for a shift from prevention-centric security toward agentic cyber resilience. Rather than seeking perfect protection, resilient systems must anticipate disruption, maintain critical functions under attack, recover efficiently, and learn continuously. We situate this shift within the historical evolution of cybersecurity paradigms, culminating in an AI-augmented paradigm where autonomous agents participate directly in sensing, reasoning, action, and adaptation across cyber and cyber-physical systems. We then develop a system-level framework for designing agentic AI workflows. A general agentic architecture is introduced, and attacker and defender workflows are analyzed as coupled adaptive processes, and game-theoretic formulations are shown to provide a unifying design language for autonomy allocation, information flow, and temporal composition. Case studies in automated penetration testing, remediation, and cyber deception illustrate how equilibrium-based design enables system-level resiliency design.
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