Categorical framework for quantum-resistant zero-trust AI security

The rapid deployment of AI models demands robust, quantum-resistant security against adversarial threats. We present a novel framework integrating post-quantum cryptography (PQC) with zero trust architecture (ZTA), formally grounded in category theory to secure AI model access. Our approach uniquely models cryptographic workflows as morphisms and trust policies as functors. This enables fine-grained, adaptive trust and micro-segmentation for lattice-based PQC primitives, offering enhanced protection against adversarial AI threats. We demonstrate efficacy through a concrete ESP32 implementation, validating crypto-agile transition with quantifiable improvements. Category theory provides rigorous proofs for AI security. Our implementation achieves significant memory efficiency: the agent uses 91.86% and the broker 97.88% of free heap after cryptographic operations. The system rejects 100% of unauthorized access attempts with sub-millisecond average latency.