Post-Quantum Cryptography: Securing AI Systems against Quantum Threats

Abstract

As artificial intelligence (AI) systems increasingly underpin critical applications in healthcare, finance, defense, and infrastructure, ensuring their security has become paramount. However, the rapid advancement of quantum computing poses a significant threat to classical cryptographic schemes that currently safeguard these systems. Quantum algorithms such as Shor’s and Grover’s threaten to undermine the confidentiality, integrity, and authentication of data and models that AI systems depend on. In this context, Post-Quantum Cryptography (PQC) has emerged as a crucial area of research aimed at developing cryptographic algorithms that are secure against both classical and quantum adversaries.

This paper investigates the intersection of quantum computing threats and AI system vulnerabilities, highlighting how current security mechanisms may fail in a post-quantum era. We explore various categories of PQC—including lattice-based, hash-based, and multivariate polynomial cryptography—and evaluate their applicability to securing AI systems, particularly in scenarios such as model distribution, federated learning, data encryption, and communication in AI-enabled edge and IoT environments.

Through analysis of emerging use cases and proposed architectures, the paper outlines how PQC can be integrated into the AI lifecycle to mitigate quantum-era threats. It also identifies key challenges such as computational overhead, integration complexity, and standardization gaps. The study concludes by outlining future research opportunities, including the development of efficient PQC protocols tailored to AI workloads, the creation of quantum-resilient AI pipelines, and the importance of interdisciplinary collaboration to secure AI’s future.