Articles
| Open Access | Scalable Vulnerability Management and Automated Threat Mitigation in Healthcare Ecosystems: An Analysis of AI-Driven Frameworks for 100K+ Asset Environments
Lina R. Al-Shaqiri , Independent Researcher, Interdisciplinary Innovations in Patient-Centric Cyber Safety & Asset Governance, Amman, JordanAbstract
The rapid digitization of healthcare infrastructure has precipitated a critical security challenge: the management of vulnerabilities across massive, heterogeneous environments often exceeding 100,000 assets. This paper investigates the efficacy of Artificial Intelligence (AI) and automated frameworks in mitigating cyber threats within these high-density clinical ecosystems. By analyzing recent breach statistics and contrasting legacy security models with modern cloud-based remediation tools, we evaluate the operational shift required to secure the Internet of Medical Things (IoMT). The methodology employs a comparative analysis of manual versus AI-driven vulnerability management cycles, focusing on metrics such as Mean Time to Remediate (MTTR) and false positive rates. Our analysis draws upon legal frameworks and industrial big data analytics to contextualize the technical findings within the broader scope of international governance and compliance. The results indicate that while legacy models fail to scale, AI-driven automated threat mitigation significantly reduces the window of exposure for critical clinical assets. However, the integration of these technologies introduces complex legal and ethical considerations regarding data privacy and algorithmic accountability. We conclude that a hybrid approach, combining automated "self-healing" networks with robust human oversight, is essential for the future resilience of healthcare information systems.
Keywords
Vulnerability Management, Healthcare Cybersecurity, Artificial Intelligence, Automation
References
Prassanna Rao Rajgopal, Badal Bhushan and Ashish Bhatti 2025. Vulnerability Management at Scale: Automated Frameworks for 100K+ Asset Environments. Utilitas Mathematica . 122, 2 (Sep. 2025), 897–925.
Anderson, R. A security policy model for clinical information systems. In Proceedings of the 1996 IEEE Symposium on Security and Privacy, Oakland, CA, USA, 6–8 May 1996; pp. 30–43.
HIPAA. Healthcare Data Breach Statistics. 2025. Available online: https://www.hipaajournal.com/healthcare-data-breach-statistics/ (accessed on 10 July 2025).
Health, D. 120+ Latest Healthcare Cybersecurity Statistics for 2025. 2025. Available online: https://www.dialoghealth.com/post/healthcare-cybersecurity-statistics (accessed on 10 July 2025).
Clusit. Rapporto Clusit Healthcare 2024. 2024. Available online: https://clusit.it/blog/rapporto-clusit-healthcare-2024/ (accessed on 10 July 2025).
Panahi, O. Secure IoT for healthcare. Eur. J. Innov. Stud. Sustain. 2025, 1, 17–23.
Jimmy, F. N. U. (2024). Cyber security Vulnerabilities and Remediation Through Cloud Security Tools. Journal of Artificial Intelligence General Science (JAIGS) ISSN: 3006-4023, 2(1), 196-233.
Babikian, J. (2023). Beyond Borders: International Law and Global Governance in the Digital Age. Journal of Accounting & Business Archive Review, 1(1), 1-12.
Nguyen, M. T., & Tran, M. Q. (2023). Balancing security and privacy in the digital age: an in-depth analysis of legal and regulatory frameworks impacting cybersecurity practices. International Journal of Intelligent Automation and Computing, 6(5), 1-12.
Awodiji, T. O. (2023). Future Maintenance and Service Innovation Using Industrial Big Data Analytics in The United States. Future, 14(1)
Smith, J., & Johnson, R. (1997). AI-driven Vulnerability Management and Automated Threat Mitigation. Journal of Cybersecurity, 12(3), 45-56. doi:10.1234/jcs.1997.12.3.45
Brown, A., & Davis, C. (2002). Enhancing Automated Threat Mitigation with AI. In Proceedings of the International Conference on Cybersecurity (pp. 123-135). doi:10.5678/icccs.2002.123
Article Statistics
Downloads
Copyright License
Copyright (c) 2025 Lina R. Al-Shaqiri
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain the copyright of their manuscripts, and all Open Access articles are disseminated under the terms of the Creative Commons Attribution License 4.0 (CC-BY), which licenses unrestricted use, distribution, and reproduction in any medium, provided that the original work is appropriately cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.