为边缘AI设计可自适应防护的3D安全层，提升系统抗攻击能力。
3D Guard-Layer: An Integrated Agentic AI Safety System for Edge Artificial Intelligence
- 通过3D集成构建动态学习的安全层，与边缘硬件共置
- 能持续监测并主动缓解网络攻击，降低安全风险
- 适合对安全性要求高的边缘AI部署场景
近年来，人工智能系统在现实世界中应用广泛，边缘人工智能（将AI直接嵌入边缘设备）的应用迅速增长。尽管已实施防护机制，但该领域安全漏洞和挑战日益突出，成为实际部署与安全性的主要障碍。本文提出一种基于3D集成的智能体式AI安全架构，引入可自适应学习的安全基础设施，能够动态识别并应对针对AI系统的攻击。系统利用与边缘计算硬件共置的优势，持续监控、检测并主动防御威胁。本地化处理与学习能力的结合，增强了对新型网络攻击的抵御能力，同时提升了系统可靠性、模块化与性能，且开销极低，仅需微小的3D集成成本。
原文摘要 · Abstract (English)
AI systems have found a wide range of real-world applications in recent years. The adoption of edge artificial intelligence, embedding AI directly into edge devices, is rapidly growing. Despite the implementation of guardrails and safety mechanisms, security vulnerabilities and challenges have become increasingly prevalent in this domain, posing a significant barrier to the practical deployment and safety of AI systems. This paper proposes an agentic AI safety architecture that leverages 3D to integrate a dedicated safety layer. It introduces an adaptive AI safety infrastructure capable of dynamically learning and mitigating attacks against the AI system. The system leverages the inherent advantages of co-location with the edge computing hardware to continuously monitor, detect and proactively mitigate threats to the AI system. The integration of local processing and learning capabilities enhances resilience against emerging network-based attacks while simultaneously improving system reliability, modularity, and performance, all with minimal cost and 3D integration overhead.
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