Artificial Intelligence and Predictive Analytics in Preventing Ransomware Attacks on Critical Healthcare Information Systems in the United States: A Strategic National Security Assessment

Authors: Zeliatu Ahmed, Aisha Mohammed Suleiman, Matilda Thompson

DOI: https://doi.org/10.37082/IJIRMPS.v14.i1.232946

Short DOI: https://doi.org/hbp5jz

Country: United States

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Abstract: Ransomware attacks on critical healthcare information systems (HIS) in the United States pose a significant threat to national security, patient safety, and the continuity of healthcare services. As cybercriminals employ increasingly sophisticated attack vectors, traditional security measures have proven insufficient in mitigating these threats. This paper explores the role of artificial intelligence (AI) and predictive analytics in enhancing the cybersecurity posture of healthcare information systems, with a particular focus on preemptive threat detection and response. Through a strategic national security assessment, this study evaluates how AI-driven models can identify ransomware patterns, predict attack vectors, and automate threat mitigation in real time. By analyzing historical ransomware incidents and leveraging machine learning algorithms, this research demonstrates the potential of predictive analytics in fortifying healthcare infrastructure against cyber threats. Furthermore, it assesses the policy and regulatory landscape governing AI implementation in cybersecurity for healthcare institutions. The findings demonstrate that AI-enabled predictive models greatly enhance the early identification of ransomware and allow healthcare systems to intercept in-processing attacks before they compromise critical data. These models successfully provide a mapping of anomalous network activities and predict probable points of entry, allowing for automated, proactive defense mechanisms. The study also highlights current gaps in policy and the pressing need for more standardized regulations to support the widespread adoption of AI in healthcare security. The findings underscore the need for a robust, AI-integrated cybersecurity framework that enhances threat intelligence, reduces response time, and ensures resilience against ransomware attacks. This study provides strategic recommendations for policymakers, healthcare administrators, and cybersecurity professionals to strengthen national security through AI-driven predictive analytics.

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Paper Id: 232946

Published On: 2026-02-19

Published In: Volume 14, Issue 1, January-February 2026

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