用智能代理构建自适应安全架构，实时防护复杂数字生态。
Adaptive Cybersecurity Architecture for Digital Product Ecosystems Using Agentic AI
- 引入自主智能体实现动态学习与上下文决策。
- 零日攻击识别率提升，响应延迟降低，检测准确率提高。
- 适合需要合规与弹性防护的云与边缘系统安全团队。
传统静态网络安全模型在当前包含云服务、API、移动平台和边缘设备的数字产品生态系统中，常面临可扩展性差、实时检测难和上下文响应不足的问题。本文提出一种基于智能体人工智能（Agentic AI）的自适应安全架构，通过部署自主目标驱动的智能体，在关键生态层级实现动态学习与上下文感知决策。该框架集成行为基线建模、去中心化风险评分与联邦威胁情报共享机制，支持自主威胁缓解、主动策略执行与实时异常检测。通过原生云环境仿真验证，系统展现出对零日攻击的有效识别能力，并能动态调整访问策略。评估结果显示，系统适应性增强，响应延迟下降，检测准确率提升。该架构为复杂数字基础设施提供智能、可扩展的安全蓝图，兼容零信任模型，有助于满足国际网络安全合规要求。
原文摘要 · Abstract (English)
Traditional static cybersecurity models often struggle with scalability, real-time detection, and contextual responsiveness in the current digital product ecosystems which include cloud services, application programming interfaces (APIs), mobile platforms, and edge devices. This study introduces autonomous goal driven agents capable of dynamic learning and context-aware decision making as part of an adaptive cybersecurity architecture driven by agentic artificial intelligence (AI). To facilitate autonomous threat mitigation, proactive policy enforcement, and real-time anomaly detection, this framework integrates agentic AI across the key ecosystem layers. Behavioral baselining, decentralized risk scoring, and federated threat intelligence sharing are important features. The capacity of the system to identify zero-day attacks and dynamically modify access policies was demonstrated through native cloud simulations. The evaluation results show increased adaptability, decreased response latency, and improved detection accuracy. The architecture provides an intelligent and scalable blueprint for safeguarding complex digital infrastructure and is compatible with zero-trust models, thereby supporting the adherence to international cybersecurity regulations.
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