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Journal of Cybersecurity in AI and Quantum Computing

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A Conceptual AI-Assisted Cyber Risk Assessment and Management Framework for Critical Energy Infrastructure

Volume 2026, Issue 2ISSN: Pending Request (Online)Journal: Journal of Cybersecurity in AI and Quantum Computing
by 

Dawood alalisalem ORCID profile ;

Khalid albudair ORCID profile ;

M M Hafizur Rahman ORCID profile

Dawood alalisalem: Department of Computer Network and Communication, College of Computer Science and Information Technology, King Faisal University, Alahsa,31982 Saudi Arabia, SAU

Khalid albudair: Department of Computer Network and Communication, College of Computer Science and Information Technology, King Faisal University, Alahsa,31982 Saudi Arabia, SAU

M M Hafizur Rahman: Department of Computer Network and Communication, College of Computer Science and Information Technology, King Faisal University, Alahsa,31982 Saudi Arabia, SAU

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Published: 2026

Pages: 150–160

Abstract

As technology advances, the digitalization of critical infrastructure has significantly increased cybersecurity risks across interconnected industrial ecosystems, particularly in the energy sector, where operational continuity and safety are essential. Traditional cyber risk assessment approaches are largely static, reactive, and incapable of effectively addressing large-scale, dynamic threat environments. To address these limitations, this study proposes a novel conceptual AI-assisted cyber risk assessment and management framework that integrates deep learning, machine learning, explainable artificial intelligence (XAI), and predictive analytics to enable adaptive, proactive, and real-time risk intelligence in energy distribution systems and smart grid networks. The novelty of the proposed framework lies in its integration of risk prediction, governance alignment, and automated mitigation into a unified intelligent architecture. Furthermore, the framework aims to improve threat prioritization, support resilient decision-making, and reduce response latency in critical energy infrastructure. By bridging artificial intelligence technologies with strategic cyber risk governance, this study introduces a forward-looking model designed to strengthen cybersecurity resilience in the evolving energy landscape.

Keywords

Artificial IntelligenceCyber Risk AssessmentEnergy Sector SecurityPredictive Risk ModelingSmart Grid SecurityExplainable AICritical Infrastructure Protection.

A Conceptual AI-Assisted Cyber Risk Assessment and Management Framework for Critical Energy Infrastructure is licensed under CC BY 4.0CCBY

References

  1. MANDYCH, O., ZAIKA, S., ZAIKA, O., ZHYLIAKOVA, O., & BLУZNІUK, Оksana. (2023). RISK MANAGEMENT OF INNOVATION ACTIVITIES IN THE DIGITAL ECOSYSTEM. Innovations in the Scientific, Technical and Social Ecosystems, 1(6), 24–45. https://doi.org/10.56378/mozs20231805
  2. Huaman, N., von Skarczinski, B., Stransky, C., Wermke, D., Acar, Y., Dreißigacker, A., & Fahl, S. (2021). A large-scale interview study on information security in and attacks against small and medium-sized enterprises. In Proceedings of the 30th USENIX Security Symposium (USENIX Security 21) (pp. 1235–1252). USENIX Association. https://www.usenix.org/conference/usenixsecurity21/presentation/huaman
  3. Cobos, E. V., Cakir, S., Straub, S., Qiang, C. Z., & Torgusson, C. (2024). A review of the economic costs of cyber incidents. World Bank, Washington, DC, USA, Rep, 193919.‏
  4. Hanisch, R., Kaiser, D., Yuan, A., Medina-Smith, A., Carroll, B., & Campo, E. (2024). NIST Research Data Framework (RDaF) (, Ed.). National Institute of Standards and Technology. https://doi.org/10.6028/nist.sp.1500-18r2
  5. Nechita, R.-M., Stochioiu, F.-P.-G., & Grecu, I. (2025). Identifying and Prioritising Factors for Effective Decision-Making in Data-Driven Organisations: A DEMATEL Approach. Systems, 13(8), 687. https://doi.org/10.3390/systems13080687
  6. Beckley, J. (2025). Advanced Risk Assessment Techniques: Merging Data-Driven Analytics with Expert Insights to Navigate Uncertain Decision-Making Processes. International Journal of Research Publication and Reviews, 6(3), 1454–1471. https://doi.org/10.55248/gengpi.6.0325.1148
  7. Kalla, D., Samaah, F., & Kuraku, Dr. S. (2021). Enhancing cyber security by predicting malwares using supervised machine learning models. International Journal of Computing and Artificial Intelligence, 2(2), 55–62. https://doi.org/10.33545/27076571.2021.v2.i2a.71
  8. Tan, Z., Nagar, U. T., He, X., Nanda, P., Liu, R. P., Wang, S., & Hu, J. (2014). Enhancing Big Data Security with Collaborative Intrusion Detection. IEEE Cloud Computing, 1(3), 27–33. https://doi.org/10.1109/mcc.2014.53
  9. Mohammad, A. S., & Pradhan, M. R. (2021). Machine learning with big data analytics for cloud security. Computers and Electrical Engineering, 96, 107527. https://doi.org/10.1016/j.compeleceng.2021.107527
  10. Sood, S. K. (2012). A combined approach to ensure data security in cloud computing. Journal of Network and Computer Applications, 35(6), 1831–1838. https://doi.org/10.1016/j.jnca.2012.07.007
  11. Kumar, P. R., Raj, P. H., & Jelciana, P. (2018). Exploring Data Security Issues and Solutions in Cloud Computing. Procedia Computer Science, 125, 691–697. https://doi.org/10.1016/j.procs.2017.12.089
  12. Alkasassbeh, M., Omoush, E. H., Almseidin, M., & Aldweesh, A. (2025). A Self-Adaptive Intrusion Detection System for Zero-Day Attacks Using Deep Q-Networks. IEEE Access, 13, 174280–174296. https://doi.org/10.1109/access.2025.3617792
  13. Chadwick, D. W., Fan, W., Costantino, G., de Lemos, R., Di Cerbo, F., Herwono, I., Manea, M., Mori, P., Sajjad, A., & Wang, X.-S. (2020). A cloud-edge based data security architecture for sharing and analysing cyber threat information. Future Generation Computer Systems, 102, 710–722. https://doi.org/10.1016/j.future.2019.06.026
  14. Stergiou, C., Psannis, K. E., Gupta, B. B., & Ishibashi, Y. (2018). Security, privacy & efficiency of sustainable Cloud Computing for Big Data & IoT. Sustainable Computing: Informatics and Systems, 19, 174–184. https://doi.org/10.1016/j.suscom.2018.06.003
  15. Bahani, K., Moujabbir, M., & Ramdani, M. (2021). An accurate fuzzy rule-based classification systems for heart disease diagnosis. Scientific African, 14, e01019. https://doi.org/10.1016/j.sciaf.2021.e01019
  16. Wei, X., & Dong, Y. (2025). A hybrid approach combining Bayesian networks and logistic regression for enhancing risk assessment. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-10291-9
  17. Li, J., Li, B., Wo, T., Hu, C., Huai, J., Liu, L., & Lam, K. P. (2012). CyberGuarder: A virtualization security assurance architecture for green cloud computing. Future Generation Computer Systems, 28(2), 379–390. https://doi.org/10.1016/j.future.2011.04.012
  18. Wang, R. (2017). Research on Data Security Technology Based on Cloud Storage. Procedia Engineering, 174, 1340–1355. https://doi.org/10.1016/j.proeng.2017.01.286
  19. Velmurugadass, P., Dhanasekaran, S., Shasi Anand, S., & Vasudevan, V. (2021). Enhancing Blockchain security in cloud computing with IoT environment using ECIES and cryptography hash algorithm. Materials Today: Proceedings, 37, 2653–2659. https://doi.org/10.1016/j.matpr.2020.08.519
  20. Wang, Z., Wang, N., Su, X., & Ge, S. (2020). An empirical study on business analytics affordances enhancing the management of cloud computing data security. International Journal of Information Management, 50, 387–394. https://doi.org/10.1016/j.ijinfomgt.2019.09.002
  21. Du, Y., Chatterjee, S., Bhattacharya, A., Dutta, A., & Halappanavar, M. (2023). Role of reinforcement learning for risk‐based robust control of cyber‐physical energy systems. Risk Analysis, 43(11), 2280–2297. Portico. https://doi.org/10.1111/risa.14104
  22. Gamaleldin, W., Attayyib, O., Alnfiai, M. M., Alotaibi, F. A., & Ming, R. (2025). A hybrid model based on CNN-LSTM for assessing the risk of increasing claims in insurance companies. PeerJ Computer Science, 11, e2830. Portico. https://doi.org/10.7717/peerj-cs.2830