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Categorical framework for quantum-resistant zero-trust AI security.

I Cherkaoui1, C Clarke2, J Horgan2

  • 1Walton Institute, South East Technological University, Waterford, Ireland. Ilias.Cherkaoui@WaltonInstitute.ie.

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|February 3, 2026
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Summary
This summary is machine-generated.

This study introduces a novel framework combining post-quantum cryptography (PQC) and zero trust architecture (ZTA) for securing AI models against advanced threats. The system ensures robust AI security with efficient, verifiable protection.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Rapid AI deployment necessitates advanced security measures against evolving adversarial threats.
  • Existing security architectures struggle to provide robust protection for AI models.
  • The need for quantum-resistant security solutions is paramount.

Purpose of the Study:

  • To present a novel framework integrating post-quantum cryptography (PQC) with zero trust architecture (ZTA) for securing AI model access.
  • To formally ground the framework in category theory for rigorous security proofs.
  • To demonstrate the framework's efficacy and efficiency in a practical implementation.

Main Methods:

  • Integration of PQC primitives with ZTA principles.
  • Formal modeling of cryptographic workflows as morphisms and trust policies as functors using category theory.
  • Implementation on an ESP32 platform to validate crypto-agile transition.
  • Evaluation of memory efficiency and access control performance.

Main Results:

  • The framework provides fine-grained, adaptive trust and micro-segmentation for lattice-based PQC.
  • Demonstrated quantifiable improvements in crypto-agile transition.
  • Achieved significant memory efficiency: agent uses 91.86% and broker 97.88% of free heap.
  • System rejects 100% of unauthorized access attempts with sub-millisecond latency.

Conclusions:

  • Category theory offers rigorous mathematical foundations for AI security.
  • The proposed PQC-ZTA framework enhances protection against adversarial AI threats.
  • The implementation validates the system's efficiency, security, and practical applicability.