AI-Enabled Cyber Resilience for Healthcare: Predictive Threat Detection and Risk Prioritization in Kingdom of Saudi Arabia Digital Hospitals

References

[1] National Cybersecurity Authority. Essential Cybersecurity Controls (ECC 2-2024). Riyadh: National Cybersecurity Authority; 2024.

[2] National Cybersecurity Authority. Cloud Cybersecurity Controls (CCC-1:2020). Riyadh: National Cybersecurity Authority; 2020.

[3] Saudi Data & AI Authority. Personal Data Protection Law. Riyadh: Saudi Data & AI Authority; 2023.

[4] Saudi Data & AI Authority. Implementing Regulations of the Personal Data Protection Law. Riyadh: Saudi Data & AI Authority; 2024.

[5] Al-Kahtani N, Alruwaie S, Al-Zahrani BM, Abumadini RA, Aljaafary A, Hariri B, et al. Digital health transformation in Saudi Arabia: a cross-sectional analysis using Healthcare Information and Management Systems Society digital health indicators. Digital Health. 2022; 8:20552076221117742.

[6] World Health Organization. Global Strategy on Digital Health 2020-2025. Geneva: World Health Organization; 2021.

[7] National Institute of Standards and Technology. The NIST Cybersecurity Framework 2.0. Gaithersburg, MD: NIST; 2024.

[8] National Institute of Standards and Technology. Artificial Intelligence Risk Management Framework (AI RMF 1.0). Gaithersburg, MD: NIST; 2023.

[9] European Union Agency for Cybersecurity. ENISA Threat Landscape: Health Sector. Athens: ENISA; 2023.

[10] He Y, Aliyu A, Evans M, Luo C. Health care cybersecurity challenges and solutions under the climate of COVID-19: scoping review. Journal of Medical Internet Research. 2021;23(4):e21747.

[11] Ewoh P, Vartiainen T, Nambisan P. Vulnerability to cyberattacks and sociotechnical solutions for health care systems: systematic literature review. Journal of Medical Internet Research. 2024;26: e46904.

[12] Elhoseny M, Thilakarathne NN, Alghamdi MI, Mahendran RK, Gardezi AA, Weerasinghe H, et al. Security and privacy issues in Medical Internet of Things: overview, countermeasures, challenges and future directions. Sustainability. 2021;13(21):11645.

[13] Huda S, Islam MR, Abawajy J, Kottala VNV, Ahmad S. A cyber risk assessment approach to federated identity management framework-based digital healthcare system. Sensors. 2024;24(16):5282.

[14] Radanliev P. AI security and cyber risk in IoT systems. Digital Society. 2024;3:1-26.

[15] Balhareth G, Alharbi A, Alqurashi M, Aldhyani THH. Optimized intrusion detection for IoMT networks with tree-based machine learning and feature selection. Scientific Reports. 2024; 14:20733.

[16] Khatun MA, Raza SM, Islam MS, Gawanmeh A. Machine learning for Healthcare-IoT security: a review. IEEE Access. 2023; 11:12101-12125.

[17] Hasegawa K, Daoud A, Sherif M, Aborode AT. Cybersecurity interventions in health care organizations in low- and middle-income countries: scoping review. Journal of Medical Internet Research. 2024;26:e51775.

[18] Aldosari B. Cybersecurity in healthcare: new threat to patient safety. Cureus. 2025;17(5):e84247.

[19] Alharbi A, Alotaibi Y, Alghamdi S. Cybersecurity governance in the healthcare sector during digital transformation. Frontiers in Public Health. 2025; 13:1703689.

[20] Di Palma G, Cafiero C, Frascino M, Romano A. AI-induced cybersecurity risks in healthcare: a narrative review. Healthcare. 2025;13(20):2558.

[21] Hosain Y, Islam MM, Alshamrani SS. XAI-XGBoost: an innovative explainable intrusion detection framework for Internet of Medical Things systems. Scientific Reports. 2025; 15:21095.

[22] Shaikh JA, Wang C, Sima MWU, Arshad M, Owais M, Hassan DSM, et al. A deep reinforcement learning-based robust intrusion detection system for securing IoMT healthcare networks. Frontiers in Medicine. 2025; 12:1524286.

[23] Siqueira LP, Carvalho JV, Reis R, Albuquerque VHC. A comprehensive survey on intrusion detection systems for Healthcare 5.0. Computers in Biology and Medicine. 2025; 184:109371.

[24] Alzakari SA, Alghamdi A, Alshammari T. Explainable artificial intelligence-based cyber resilience in smart healthcare systems. Scientific Reports. 2025; 15:18770.

[25] Health Sector Transformation Program. Health Sector Transformation Program Delivery Plan 2021-2025. Riyadh: Kingdom of Saudi Arabia; 2021.

[26] AlDulijand NA, Alshammari F, Alotaibi M, Alanzi T. Sustainable healthcare resilience: disaster preparedness and business continuity planning in Saudi hospitals. Sustainability. 2024;16(1):198.

[27] International Organization for Standardization. ISO/IEC 27001:2022 Information security, cybersecurity and privacy protection: information security management systems. Geneva: ISO; 2022.

[28] International Organization for Standardization. ISO/IEC 27005:2022 Information security, cybersecurity and privacy protection: guidance on managing information security risks. Geneva: ISO; 2022.

[29] International Organization for Standardization. ISO/IEC 42001:2023 Information technology: artificial intelligence management system. Geneva: ISO; 2023.

[30] Cybersecurity and Infrastructure Security Agency. Cross-Sector Cybersecurity Performance Goals. Washington, DC: CISA; 2023.

@article{1719759,
      author = {Muhammad Umer Majeed},
      title = {AI-Enabled Cyber Resilience for Healthcare: Predictive Threat Detection and Risk Prioritization in Kingdom of Saudi Arabia Digital Hospitals},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {1},
      pages = {1520-1531},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1719759.pdf},
      abstract = {Kingdom of Saudi Arabia hospitals are moving rapidly toward integrated electronic records, connected clinical devices, cloud hosting, virtual care, and analytics-enabled operations. These capabilities improve access and continuity, yet they also create a larger attack surface in which ransomware, identity compromise, data leakage, device manipulation, and supply-chain disruption may affect patient safety as well as information security. This review develops an AI-enabled cyber-resilience model for Kingdom of Saudi Arabia digital hospitals, with emphasis on predictive threat detection and clinically informed risk prioritisation. The objectives were to synthesise recent evidence on healthcare cyber threats, examine how machine learning and explainable analytics can support security operations, align cyber-resilience practices with Kingdom of Saudi Arabia regulatory expectations, and propose a practical model suitable for high-availability clinical environments. A structured narrative review was undertaken using targeted searches of Scopus, Web of Science, PubMed, IEEE Xplore, and selected regulatory sources published from 2020 to 2025. Evidence was coded into five themes: digital-hospital attack surface, predictive detection, risk scoring, governance, and operational resilience. The synthesis indicates that AI can improve early detection when behavioural baselines, device telemetry, identity signals, vulnerability intelligence, and clinical criticality are fused in a governed security data layer. However, AI also introduces model drift, adversarial manipulation, privacy exposure, and automation bias. The proposed model therefore integrates zero-trust identity, monitored segmentation, AI-assisted security operations, clinician-aware triage, privacy controls, and disaster recovery into a single risk-prioritisation cycle. The review concludes that AI-enabled resilience should be treated as a socio-technical operating model rather than a tool purchase.},
      keywords = {AI-Enabled Cybersecurity, Cyber Resilience, Kingdom of Saudi Arabia Hospitals, Predictive Threat Detection, Risk Prioritisation, Digital Health, Electronic Health Records, Internet of Medical Things},
      month = {July},
      doi = {https://doi.org/10.64388/IREV10I1-1719759}
  }