The rapid deployment of AI models necessitates robust, quantum-resistant security, particularly against adversarial threats. Here, we present a novel integration of post-quantum cryptography (PQC) and zero trust architecture (ZTA), formally grounded in category theory, to secure AI model access. Our framework uniquely models cryptographic workflows as morphisms and trust policies as functors, enabling fine-grained, adaptive trust and micro-segmentation for lattice-based PQC primitives. This approach offers enhanced protection against adversarial AI threats. We demonstrate its efficacy through a concrete ESP32-based implementation, validating a crypto-agile transition with quantifiable performance and security improvements, underpinned by categorical proofs for AI security. The implementation achieves significant memory efficiency on ESP32, with the agent utilizing 91.86% and the broker 97.88% of free heap after cryptographic operations, and successfully rejects 100% of unauthorized access attempts with sub-millisecond average latency.

翻译：人工智能模型的快速部署亟需强大且抗量子的安全性，特别是针对对抗性威胁。本文提出了一种将后量子密码学与零信任架构相结合的新颖方法，该方法以范畴论为形式化基础，用于保护人工智能模型的访问。我们的框架独特地将密码工作流建模为态射，将信任策略建模为函子，从而为基于格的后量子密码原语实现了细粒度、自适应的信任与微隔离。该方法显著增强了对抗性人工智能威胁的防护能力。我们通过一个基于ESP32的具体实现验证了其有效性，展示了在可量化性能与安全性提升下的密码敏捷性过渡，并以范畴论证明为人工智能安全提供理论支撑。该实现在ESP32上实现了显著的内存效率：密码操作后，代理占用91.86%的可用堆内存，代理服务器占用97.88%的可用堆内存，并以亚毫秒级平均延迟成功拦截100%的未授权访问尝试。