Categorical Framework for Quantum-Resistant Zero-Trust AI Security

TL;DR

This paper introduces a category-theoretic framework combining post-quantum cryptography and zero trust architecture to enhance AI security against adversarial threats, validated through an efficient ESP32 implementation.

Contribution

It presents a novel categorical model for integrating PQC and ZTA in AI security, enabling adaptive trust policies and micro-segmentation with formal proofs.

Findings

Achieves high memory efficiency on ESP32 hardware.

Successfully rejects all unauthorized access attempts.

Maintains sub-millisecond latency for cryptographic operations.

Abstract

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.