The advent of quantum computing introduces fundamental challenges to contemporary cybersecurity, particularly by undermining the security assumptions of widely deployed cryptographic schemes. In parallel, the increasing sophistication, stealth, and adaptability of modern cyber threats expose the limitations of traditional, reactive security monitoring approaches. These developments motivate a shift toward defense strategies that are both quantum-resilient and intelligence-driven. This paper presents a comprehensive review of the convergence between post-quantum cryptography (PQC) and AI-driven threat hunting, focusing on the design and evolution of quantum-resilient defense frameworks. Rather than emphasizing empirical performance comparisons or speculative quantitative claims, the review adopts a concept-centric and analytically grounded perspective. It examines the foundational principles of post-quantum cryptography, the role of artificial intelligence in proactive threat hunting, and the emerging need to integrate cryptographic assurance with adaptive, learning-based security mechanisms. Existing architectural models, trust and risk abstractions, and formal security properties are analyzed to highlight how AI techniques can complement PQC by enabling continuous threat inference, contextual awareness, and adaptive response in post-quantum environments. The paper further synthesizes current research into a unified conceptual framework that captures the interaction between quantum-resistant cryptographic primitives and AI-based threat hunting processes. Key research gaps and open challenges are identified, including issues related to scalability, explainability, validation under future threat models, and the co-evolution of cryptographic and intelligent defense mechanisms. By consolidating fragmented research directions into a coherent analytical narrative, this review aims to serve as a foundational reference for future work on integrated, quantum-resilient cybersecurity architectures.
The advent of quantum computing introduces fundamental challenges to contemporary cybersecurity, particularly by undermining the security assumptions of widely deployed cryptographic schemes. In parallel, the increasing sophistication, stealth, and adaptability of modern cyber threats expose the limitations of traditional, reactive security monitoring approaches. These developments motivate a shift toward defense strategies that are both quantum-resilient and intelligence-driven. This paper presents a comprehensive review of the convergence between post-quantum cryptography (PQC) and AI-driven threat hunting, focusing on the design and evolution of quantum-resilient defense frameworks. Rather than emphasizing empirical performance comparisons or speculative quantitative claims, the review adopts a concept-centric and analytically grounded perspective. It examines the foundational principles of post-quantum cryptography, the role of artificial intelligence in proactive threat hunting, and the emerging need to integrate cryptographic assurance with adaptive, learning-based security mechanisms. Existing architectural models, trust and risk abstractions, and formal security properties are analyzed to highlight how AI techniques can complement PQC by enabling continuous threat inference, contextual awareness, and adaptive response in post-quantum environments. The paper further synthesizes current research into a unified conceptual framework that captures the interaction between quantum-resistant cryptographic primitives and AI-based threat hunting processes. Key research gaps and open challenges are identified, including issues related to scalability, explainability, validation under future threat models, and the co-evolution of cryptographic and intelligent defense mechanisms. By consolidating fragmented research directions into a coherent analytical narrative, this review aims to serve as a foundational reference for future work on integrated, quantum-resilient cybersecurity architectures.
The rapid advancement of quantum computing poses a significant threat to the confidentiality and integrity of classified and defense communications. Quantum algorithms have the potential to undermine widely deployed public key cryptographic schemes, exposing sensitive military and governmental information to interception and decryption. Foundational work in quantum cryptography has demonstrated that the principles of quantum mechanics can be leveraged to achieve fundamentally secure key distribution, offering a promising countermeasure to quantum-enabled attacks (Bennett & Brassard, 2014; Ekert, 1991). As defense communication systems increasingly rely on longterm data confidentiality, the urgency of transitioning toward quantum-resilient security architectures has become a strategic priority. Despite their widespread adoption, classical cryptographic mechanisms are increasingly vulnerable in the post-quantum era. Studies on post-quantum cryptography have shown that many conventional encryption and key exchange algorithms are susceptible to quantum adversaries, necessitating the development and standardization of new cryptographic primitives (Chen et al., 2016). Ongoing standardization efforts further highlight the complexity of migrating large-scale, mission-critical systems to quantum-safe solutions, particularly within defense environments where interoperability, reliability, and assurance requirements are stringent (Alagic et al., 2025). In parallel, artificial intelligence has emerged as a powerful enabler of advanced security analytics. Machine learning and deep learning techniques enhance intrusion detection, anomaly identification, and adaptive response capabilities within complex communication infrastructures (Buczak & Guven, 2016). Recent research also demonstrates that AI can be applied directly within quantum cryptographic systems, for example by improving signal processing, error correction, and performance optimization in quantum key distribution implementations (Chin et al., 2021). The convergence of quantum cryptography and AIdriven analytics therefore offers a complementary and proactive approach to securing defense communications against both classical and quantum threats. This paper analyzes the integration of quantum cryptography and artificial intelligence as a unified framework for protecting classified and defense communications. It synthesizes advances in quantum key distribution, post-quantum cryptography, and AI-based security analytics, and proposes an integrated perspective that enhances resilience, detection capability, and operational robustness. The study highlights key implications for defense communication systems, emphasizing how AI-enhanced quantum security architectures can support long-term confidentiality, adaptive defense, and strategic cyber resilience in the evolving threat landscape (Sarker et al., 2021).
The rapid advancement of quantum computing presents both unprecedented opportunities and significant threats to modern cybersecurity. As quantum processors progress toward practical large-scale deployment, widely used cryptographic systems, particularly RSA, ECC, and other public-key mechanisms, face the risk of becoming obsolete. This review examines how quantum technologies can be strategically leveraged to enhance cybersecurity while simultaneously addressing the vulnerabilities introduced by quantum-enabled attacks. A systematic analysis of 35-40 peer-reviewed studies was conducted to evaluate developments in post-quantum cryptography, quantum-resistant security protocols, Quantum Key Distribution (QKD), and hybrid classical–quantum defense architectures. The findings reveal a rapidly evolving research landscape focused on resilient lattice-based cryptography, scalable quantum-safe communication frameworks, and early-stage quantum machine learning applications for intrusion detection. The review highlights both the promise and current limitations of quantum-driven security solutions, including hardware constraints, integration challenges, and the need for global standardization. The study underscores the urgency for governments, industries, and researchers to adopt quantum-ready cybersecurity strategies and accelerate the transition to quantum-resilient infrastructures.
Quantum computing and artificial intelligence (AI) converge as a phenomenon that is changing cybersecurity defense mechanisms, providing both unprecedented opportunities and emergent challenges. With classical encryption becoming more susceptible to quantum-driven attacks, resilient quantum-safe protocols and AI-driven threat detection systems have become highly demanded. Recent studies point to the disruptive nature of quantum technologies in compromising traditional cryptography systems and, at the same time, provide novel countermeasures to disruptive technologies in the form of quantum cryptography, blockchain, and adaptive machine learning systems. The rising AI improves the active threat-hunting, zero-trust, and predictive analytics to detect advanced persistent threats in real-time across cloud, hybrid, and cyber-physical ecosystems. In the meantime, moral issues, institutional preparedness, and technological asymmetry pose obstacles to extensive adoption. The potential for a future-proof digital infrastructure with strategic integration of AI and quantum computing, advancing national security, and collaborating with partners around the world across the rapidly changing cyber threat could be a path forward. This article aims to critically assess the state of cybersecurity in the future by reviewing different academic and professional perceptions and experiences.
Abstract : Objective: The rapid development of quantum computing poses an existential threat to classical cryptographic systems that currently secure global digital infrastructure. In direct response to this quantum threat, post-quantum cryptography (PQC) has emerged as a critical field dedicated to designing algorithms resistant to quantum attacks. Simultaneously, artificial intelligence (AI) — particularly machine learning (ML) and deep learning (DL) — has demonstrated promising and emerging capabilities across cybersecurity domains, including cryptography. Methods: This systematic review was conducted by searching IEEE Xplore, ACM Digital Library, Springer Link, Google Scholar, Scopus, and Web of Science using targeted keywords related to AI and PQC, covering literature published between 2015 and 2025. A total of 62 peer-reviewed studies meeting predefined inclusion criteria were analysed. Results: A total of 38 key studies were identified and analysed across four principal application domains: algorithm design and parameter optimization (31.6%), cryptanalysis and security assessment (26.3%), side-channel attack detection and defense (23.7%), and secure deployment on resource-constrained devices (18.4%). Practical case studies demonstrate measurable performance gains, including a 27% reduction in key exchange time reported in a specific study [60] and 98.3% accuracy in side-channel attack detection reported in a specific study. Conclusions: The synergy between AI and PQC represents a pivotal frontier in cybersecurity. This review provides a structured foundation for future interdisciplinary research in quantum-safe intelligent systems and identifies explainable AI (XAI) integration as the most critical open research direction.
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.
Objective: The rapid development of quantum computing poses an existential threat to classical cryptographic systems that currently secure global digital infrastructure. In direct response to this quantum threat, post-quantum cryptography (PQC) has emerged as a critical field dedicated to designing algorithms resistant to quantum attacks. Simultaneously, artificial intelligence (AI) — particularly machine learning (ML) and deep learning (DL) — has demonstrated promising and emerging capabilities across cybersecurity domains, including cryptography. Methods: This systematic review was conducted by searching IEEE Xplore, ACM Digital Library, Springer Link, Google Scholar, Scopus, and Web of Science using targeted keywords related to AI and PQC, covering literature published between 2015 and 2025. A total of 62 peer-reviewed studies meeting predefined inclusion criteria were analysed. Results: A total of 38 key studies were identified and analysed across four principal application domains: algorithm design and parameter optimization (31.6%), cryptanalysis and security assessment (26.3%), side-channel attack detection and defense (23.7%), and secure deployment on resource-constrained devices (18.4%). Practical case studies demonstrate measurable performance gains, including a 27% reduction in key exchange time reported in a specific study [60] and 98.3% accuracy in side-channel attack detection reported in a specific study. Conclusions: The synergy between AI and PQC represents a pivotal frontier in cybersecurity. This review provides a structured foundation for future interdisciplinary research in quantum-safe intelligent systems and identifies explainable AI (XAI) integration as the most critical open research direction.