Transforming European Cybersecurity: AI-Powered Threat Analysis, Quantum Age, Blockchain/Crypto Risks, and Regulatory Strategies

Year 2025, Volume: 17 Issue: 2, 81 - 93, 05.09.2025

Recep Arslan , Turgut Özseven , Metin Mutlu Aydın

https://doi.org/10.24107/ijeas.1619600

Abstract

European cybersecurity is rapidly evolving to address complex and emerging threats fueled by advancements in technology. AI-powered threat analysis has become a cornerstone, enabling faster detection of anomalies, predictive threat modeling, and real-time incident response. As Europe enters the quantum age, cybersecurity strategies are increasingly focused on quantum-resistant encryption to protect critical infrastructure and sensitive data from future quantum attacks. Simultaneously, the rise of blockchain technologies and cryptocurrencies introduces new vulnerabilities, such as smart contract exploits and decentralized finance (DeFi) fraud, requiring targeted regulatory oversight. In response, the EU is strengthening its regulatory frameworks, such as the NIS2 Directive and the Digital Operational Resilience Act (DORA), to ensure a harmonized, proactive approach to cybersecurity governance, resilience, and accountability across sectors. This multifaceted strategy reflects Europe’s commitment to safeguarding digital sovereignty and fostering trust in its digital ecosystem. The study deals with the transformation of the European cyber security ecosystem within the framework of artificial intelligence (AI) supported threat analysis. The paper discusses the security risks that arise in the quantum and post-quantum era, the possibility of blockchain/crypto systems being broken by quantum computers, the limitations of the existing data set, and the need for human-like thinking skills. In addition, the European Union's (EU) cybersecurity policies, data privacy principles, ethical standards, transparency, accountability, and human-centered AI design approaches are examined within the scope of the EU's global norm-setting role. This article also aims to shed light on the strategic steps that will shape the future of AI-powered cyber defense. Study shows that Europe should develop artificial intelligence (AI)-powered cybersecurity solutions in its preparations for the post-quantum era, it also should invest in AI models that transcend current data set limits and have humanoid thinking capacities.

Keywords

Cyber security , artificial intelligence , post-quantum , blockchain , crypto , data privacy , regulatory strategies

References

• Betz, D.J., Stevens, T., Cyberspace and the State: Toward a Strategy for Cyber-Power, Routledge, 2013.
• Nye, J.S., Deterrence and dissuasion in cyberspace, International Security, 41(3), 44–71, 2016.
• Kshetri, N., The quest to cyber superiority: Cybersecurity regulations, frameworks, and strategies of major economies, Journal of Cyber Policy, 3(1), 1–21, 2018.
• Bendiek, A., The EU as a force for peace in international cyber diplomacy, International Cyber Norms: Legal, Policy & Industry Perspectives, 31–42, 2018.
• Mendes, C., Rios, T.N., Explainable Artificial Intelligence and Cybersecurity: A Systematic Literature Review, arXiv, 2023. https://arxiv.org/abs/2303.01259
• Ravi, C., Quantum computing and cybersecurity: Systematic review of algorithms, challenges, and emerging solutions, in P.K. Pattnaik, M.R. Kabat, K.S. Lenka (Eds.), Smart and Sustainable Technologies for Resilient Infrastructure, Springer, 233–246, 2025.
• Ramos, S., Ellul, J., Blockchain for artificial intelligence (AI): Enhancing compliance with the EU AI Act through distributed ledger technology, AI and Ethics, 3, 645–660, 2023.
• Novelli, C., Quarta, L., Di Martino, A., Haker, C., Kuczerawy, A., Valcke, P., Van Alsenoy, B., Generative AI in EU law: Liability, privacy, intellectual property, and cybersecurity, arXiv, 2024. https://arxiv.org/abs/2401.07348
• Financial Times, EU plans to bridge finance gap for quantum computing, 2025. https://www.ft.com/content/57b43891-a717-4d7f-87c7-24dc8cde8b9f [Accessed: 6 March 2025].
• Buczak, A.L., Guven, E., A survey of data mining and machine learning methods for cyber security intrusion detection, IEEE Communications Surveys & Tutorials, 18(2), 1153–1176, 2016.
• Sommer, R., Paxson, V., Outside the closed world: On using machine learning for network intrusion detection, IEEE Security & Privacy, 8(3), 48–54, 2010.
• Goodfellow, I., Bengio, Y., Courville, A., Deep Learning, MIT Press, 2016.
• Marcus, G., Davis, E., Rebooting AI: Building Artificial Intelligence We Can Trust, Pantheon Books, 2019.
• Javaid, A., Niyaz, Q., Sun, W., Alam, M., A deep learning approach for network intrusion detection system, Proc. 9th EAI Int. Conf. on Bio-inspired Information and Communications Technologies, 21–26, 2016.
• Alazab, M., Awajan, A., Mesleh, A., Abdallah, A., Al-Qerem, A., Gupta, B.B., COVID-19 and cybersecurity: Threats, opportunities, and future directions, International Journal of Information Management, 55, 102201, 2020.
• Huang, L., Joseph, A.D., Nelson, B., Rubinstein, B.I.P., Tygar, J.D., Adversarial machine learning, Proc. 4th ACM Workshop on Security and Artificial Intelligence, 43–58, 2011.
• April, K.A., Nevill-Manning, C., Hanekom, S., Explainable Artificial Intelligence (XAI) for cybersecurity: A conceptual framework, Journal of Cyber Security Technology, 5(4), 253–270, 2021.
• Taddeo, M., The limits of encryption, Nature Electronics, 2(9), 374–375, 2019.
• Mosca, M., Cybersecurity in an Era with Quantum Computers: Will We Be Ready?, IEEE Security & Privacy, 16(5), 38–41, 2018. https://doi.org/10.1109/MSP.2018.3761722
• NIST, Post-Quantum Cryptography Standardization, National Institute of Standards and Technology, 2020.
• European Central Bank (ECB), Cyber resilience oversight expectations for financial market infrastructures, 2020. https://www.ecb.europa.eu/ [Accessed: Jan. 1, 2025].
• ENISA (European Union Agency for Cybersecurity), ENISA Threat Landscape 2022, 2022. https://www.enisa.europa.eu/ [Accessed: Jan. 1, 2025].
• ESMA (European Securities and Markets Authority), ESMA Report on Trends, Risks and Vulnerabilities, 2022. https://www.esma.europa.eu/ [Accessed: Jan. 1, 2025].
• Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J.C., Barends, R., et al., Quantum supremacy using a programmable superconducting processor, Nature, 574(7779), 505–510, 2019.
• Preskill, J., Quantum computing in the NISQ era and beyond, Quantum, 2, 79, 2018.
• Krinner, S., Lacroix, N., Remm, A., Di Paolo, A., Genest-Marcil, A., Lazar, S., et al., Realizing repeated quantum error correction in a distance-three surface code, Nature, 605(7911), 669–674, 2022.
• Zhou, L., Wang, S.T., Choi, S., Pichler, H., Lukin, M.D., Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near-term devices, Physical Review X, 10(2), 021067, 2020.
• Bharti, K., Cervera-Lierta, A., Kyaw, T.H., Haug, T., Alperin-Lea, S., Anand, A., et al., Noisy intermediate-scale quantum algorithms, Reviews of Modern Physics, 94(1), 015004, 2022.
• Zhang, T., Liu, Z., Wong, J., Artificial Intelligence for Cybersecurity: Threat Detection and Prevention, IEEE Access, 9, 12567–12583, 2021. https://doi.org/10.1109/ACCESS.2021.3080423
• Smith, J., Lee, A., Challenges in Data Labeling for Machine Learning: Impacts on Model Accuracy, Data Science and Machine Learning Applications, 3(1), 45–67, 2019. https://doi.org/10.1007/s00160-019-0123-8
• Chen, X., Lu, X., Wang, Y., Li, H., Post-Quantum Cryptography and Its Implications, ACM Computing Surveys, 53(4), 1–37, 2020. https://doi.org/10.1145/3417984
• Johnson, R., Ahmed, M., Patel, S., Anomaly Detection in Cybersecurity: Machine Learning Approaches and Challenges, Journal of Cybersecurity and Privacy, 1(2), 150–172, 2020.
• Fernandez-Carames, T.M., Fraga-Lamas, P., Towards post-quantum blockchain: A review on blockchain cryptography resistant to quantum computing attacks, IEEE Access, 8, 21091–21116, 2020.
• Hüppönen, J., Quantum Computing and Its Threats to Blockchain Security, Journal of Emerging Technologies, 5(3), 45–62, 2021. https://doi.org/10.1016/j.jemt.2021.05.003
• ISO/IEC, ISO/IEC 18033-6: Post-Quantum Cryptography Standards, International Organization for Standardization, 2021.
• Veale, M., Borgesius, F.Z., Demystifying Hybrid Encryption: A Transition Strategy for Quantum Security, Cybersecurity and Privacy Journal, 3(1), 78–95, 2021.
• European Commission, EU Security Union Strategy, 2020. https://ec.europa.eu/ [Accessed: Jan. 1, 2025].
• European Parliament and Council, General Data Protection Regulation (GDPR), Regulation (EU) 2016/679, 2016. https://eur-lex.europa.eu/ [Accessed: Jan. 1, 2025].
• Bradford, A., The Brussels Effect: How the European Union Rules the World, Oxford University Press, 2020.
• Cavelty, M.D., Breaking the cyber-security dilemma: Aligning security needs and removing vulnerabilities, Science and Engineering Ethics, 20(3), 701–715, 2014.
• Floridi, L., Taddeo, M., What is data ethics?, Philosophical Transactions of the Royal Society A, 374(2083), 2016.
• Mittelstadt, B.D., Allo, P., Taddeo, M., Wachter, S., Floridi, L., The ethics of algorithms: Mapping the debate, Big Data & Society, 3(2), 2016.
• Wachter, S., Mittelstadt, B., Floridi, L., Why a right to explanation of automated decision-making does not exist in the general data protection regulation, International Data Privacy Law, 7(2), 76–99, 2017.
• Kaljulaid, K., Ethical AI and Public Trust, Journal of AI Policy, 7(1), 45–63, 2024.