The article examines key issues related to ensuring the security of artificial intelligence systems in the context of digital economic transformation and the development of quantum computing technologies. It is shown that the largescale integration of AI into economically significant processes increases cryptographic, informational, and institutional risks associated with the potential vulnerability of classical security mechanisms to quantum attacks. The study analyzes major threats to AI systems, including cryptographic weaknesses, risks of data and machine learning model compromise, and the longterm «store now, decrypt later» threat scenario. Quantumresistant approaches to protecting artificial intelligence systems are systematized, covering postquantum cryptography, hybrid security schemes, and architectural solutions for AI platforms. Special attention is paid to the economic and institutional aspects of AI security, emphasizing the need for proactive adaptation of cybersecurity mechanisms and the development of regulatory frameworks that account for emerging quantum threats. Based on the analysis, authorial recommendations are proposed to enhance the resilience of artificial intelligence systems and mitigate longterm risks to the digital economy.
The article examines key issues related to ensuring the security of artificial intelligence systems in the context of digital economic transformation and the development of quantum computing technologies. It is shown that the largescale integration of AI into economically significant processes increases cryptographic, informational, and institutional risks associated with the potential vulnerability of classical security mechanisms to quantum attacks. The study analyzes major threats to AI systems, including cryptographic weaknesses, risks of data and machine learning model compromise, and the longterm «store now, decrypt later» threat scenario. Quantumresistant approaches to protecting artificial intelligence systems are systematized, covering postquantum cryptography, hybrid security schemes, and architectural solutions for AI platforms. Special attention is paid to the economic and institutional aspects of AI security, emphasizing the need for proactive adaptation of cybersecurity mechanisms and the development of regulatory frameworks that account for emerging quantum threats. Based on the analysis, authorial recommendations are proposed to enhance the resilience of artificial intelligence systems and mitigate longterm risks to the digital economy.
Rapid advances in quantum computing and artificial intelligence (AI) are reshaping cybersecurity by enhancing defensive capabilities while simultaneously introducing unprecedented threats to existing cryptographic systems. This paper examines the convergence of quantum computing and AI within the context of Pakistan’s emerging digital economy and its distinct geopolitical and environmental security challenges. As Pakistan continues to rely on classical cryptographic standards such as RSA and ECC, it faces growing risks from quantum-enabled attacks and increasingly sophisticated AI-driven cyber threats. Drawing on local literature, recent state-level cyber incidents in 2026, and emerging defense strategies, this study highlights the need for quantum-safe security frameworks, stronger data protection standards, and AI-driven cyber resilience in developing economies.
With the rapid advancement of artificial intelligence (AI) and quantum computing, cybersecurity threats have evolved, giving rise to adversarial quantum attacks. These attacks exploit the vulnerabilities of AI models using quantum algorithms, posing a significant risk to data security, model robustness, and decision-making systems. This paper presents a comprehensive review of adversarial quantum attacks on AI, analyzing their mechanisms, potential impacts, and countermeasures. It explores how quantum computing can enhance adversarial attacks by accelerating the generation of adversarial examples, breaking cryptographic protections, and undermining AI model integrity. Additionally, the study examines different attack vectors, including quantum-enhanced adversarial perturbations, quantum machine learning (QML) vulnerabilities, and quantum decryption threats. The paper also discusses defensive strategies such as quantum-resistant AI models, quantum cryptographic defenses, and hybrid quantum-classical security frameworks that can mitigate these risks. By evaluating existing research and emerging trends, this review provides insights into the growing intersection of AI security and quantum computing. The findings emphasize the urgent need for robust quantum-aware AI security frameworks to safeguard AI-driven systems in the quantum era. Future research directions include developing quantum-adaptive AI models, post-quantum cryptographic techniques, and real-world applications of quantum-safe AI architectures. This review aims to contribute to the ongoing discourse on ensuring AI resilience against adversarial quantum threats, paving the way for a secure and quantum-resistant future.
In the modern world, the financial sector faces a wide range of extraordinary events, from market shocks and geopolitical tensions to pandemics and cyberattacks. These diverse challenges significantly impact banks. Recently, particular attention has been drawn to the influence of artificial intelligence (AI) and quantum computing (QC) technologies on the stability of the financial system. This article analyzes the potential areas of impact of quantum technologies on the financial sphere in general and the resilience of financial institutions in particular.It's determined that quantum technologies can simultaneously pose both threats and new opportunities. Specifically, the article analyzes the potential of quantum technologies to break existing cryptographic algorithms that protect the confidential data of financial institutions' clients. This threat is exacerbated by the possibility of "harvest now, decrypt later" attacks, where malicious actors intercept data today with the intention of decrypting it in the future. This presents challenges and threats for online/mobile banking, payment operations, and corporate communications. Among the positive impacts of quantum technologies on banking, the article highlights improved financial risk assessment, particularly enhanced accuracy in stress testing, portfolio management, and derivatives pricing, by solving problems inaccessible to classical computing. The potential of quantum machine learning for fraud detection is also noted. Attention is given to the financial sector's response to these challenges. Initiatives for developing quantum-resistant (post-quantum) cryptography are considered as the primary method for mitigating the risks associated with the proliferation of quantum computing, along with new cryptographic protocols and quantum key distribution as a new opportunity to enhance security. The article emphasizes the responsibility of central banks in forming quantum-readiness strategies, including investments in post-quantum cryptography, expertise development, and the adaptation of regulatory frameworks. The conclusion drawn is that although the immediate benefits of quantum technologies may be modest, their long-term transformative potential for the financial sector is immense.
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.
Rapid advances in artificial intelligence (AI) and quantum computing can potentially transform cybersecurity. It explores the synergistic integration to improve cyber defenses. The ability of AI to analyze data, detect anomalies, and perform predictive analytics, combined with the potential of quantum computing to solve complex cryptographic problems, provides a strong framework for today's cybersecurity challenges. Using quantum algorithms and machine learning techniques, it aims to strengthen cryptographic methods, optimize threat detection systems, and develop flexible defense protocols against sophisticated cyber attacks. The study also examines the implications of quantum cryptography and the role of AI in managing quantum-generated data It discusses the challenges and ethical considerations associated with applying these advanced technologies in cybersecurity infrastructures. The findings suggest that the convergence of AI and quantum computing can significantly improve the effectiveness and adaptability of cybersecurity measures, paving the way for a secure digital future.
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.