Agentic-ZTA：用多智能体自动执行零信任访问控制
Agentic-ZTA: A Multi-Agent Architecture for Autonomous Zero Trust Enforcement
一篇把多智能体接入零信任安全的具体方案，策略检索加信任打分，测试床上做到 95% 准确率，做安全方向的可以看看。
arXiv 论文 Agentic-ZTA 提出基于 NIST SP 800-207 零信任架构的多智能体框架，把策略知识放进 RAG 管道，推理时检索 top-k 相关策略并嵌入智能体提示词。访问请求由 PEP 拦截并附加上下文元数据，先由领域专用智能体评估，必要时再调用辅助智能体。各智能体的信任分数经信任算法聚合，输出最终访问决策并持续验证。在测试床的访问控制场景中，该框架达到 95.0% 准确率、93.9% 精确率和 96.3% 召回率。
Agentic-ZTA: A Multi-Agent Architecture for Autonomous Zero Trust Enforcement
Agentic AI is emerging as a promising paradigm for automating complex cybersecurity decisions, yet its use in enforcing zero trust introduces significant challenges in safety, reliability, and policy compliance. This paper presents Agentic AI based zero trust architecture (Agentic-ZTA) that operationalizes the NIST SP 800-207 ZTA architecture control loop through coordinated multi- agent decision pipeline. In the proposed framework, policy knowledge is embedded into a retrieval-augmented generation pipeline and retrieved at inference time as top-k relevant policies. Access requests are intercepted by the Policy Enforcement Point (PEP), enriched with contextual metadata. The request context is routed to a policy engine agent which invokes domain-specialized core agents first followed by supporting agents, if further evaluation needed. AI agents reason over access context, policy constraints and determine trust. The retrieved policies are embedded into agent prompt during inference time and agentic trust scores are aggregated and evaluated by a trust-algorithm, producing the final access decision for enforcement under continuous verification. We implement Agentic-ZTA in a testbed and evaluate it on representative access-control use cases scenarios. Our Agentic-ZTA framework achieves 95.0% accuracy, 93.9% precision, and 96.3% recall, and demonstrate the feasibility of enforcing zero trust using AI agents.
