AI-Driven Self-Healing Cybersecurity Architectures with Embedded Adaptive Instructional Systems for Protecting the U.S. Healthcare Infrastructure Against Advanced Persistent Threats (APTs)

Ekundayo Peter Buremoh

Department of Computing and Games, Teesside University Middlesborough, UK.

dayourburemoh@gmail.com

Ayomide Bolaji-Fayehun

Plymouth Business School, University of Plymouth, Plymouth, UK.

midefayehun@gmail.com

Folashade Agbolade

Learning Technologies, Design and School Library Media, Towson University, Maryland, USA.

Fagbola1@students.towson.edu

https://orcid.org/0009-0009-0636-1003

Chioma Ogechukwu Obi

Department of Health Sciences, Towson University, Maryland, USA.

chiomaoguchi@gmail.com

https://orcid.org/0009-0005-6102-6954

Emmanuel Ologun

Department of Computer Science and Engineering, University of Fairfax, USA.

ologune@students.ufairfax.edu

Abstract
The increasing sophistication of cyber threats, particularly Advanced Persistent Threats (APTs), poses a serious risk to healthcare infrastructure, where system disruption may directly affect patient safety and service delivery. Traditional cybersecurity approaches, which rely heavily on reactive and signature-based mechanisms, are increasingly inadequate in addressing the dynamic, stealthy, and evolving nature of these attacks. This study examines the role of Artificial Intelligence (AI) in transforming cybersecurity practices and proposes a more adaptive and resilient approach to protecting healthcare systems. Using a critical review methodology, the study synthesizes existing literature on AI-driven cybersecurity, self-healing system architectures, behavioral threat detection, and adaptive cybersecurity training. The review identifies key limitations in current approaches, including fragmented system designs, over-reliance on detection without recovery capabilities, high false positive rates, limited integration of human factors, and insufficient consideration of healthcare-specific constraints. While AI has significantly improved threat detection and response speed, existing solutions often lack the ability to autonomously recover from attacks and continuously adapt to new threat patterns. To address these gaps, the study proposes an integrated conceptual framework that combines AI-driven threat detection, self-healing cybersecurity mechanisms, and embedded adaptive instructional systems within a continuous feedback loop. The framework is designed to enable real-time threat identification, automated response and recovery, and ongoing system and user learning. By embedding human-centered learning into the cybersecurity architecture, the model also addresses one of the most persistent vulnerabilities in cyber defense, human error. This study contributes to the literature by advancing a holistic, adaptive, and healthcare-focused cybersecurity model that moves beyond isolated solutions toward a unified and intelligent defense system.

Keywords: Self-Healing Cybersecurity Architectures, AI-Driven Cybersecurity, Adaptive Instructional Systems, Advanced Persistent Threats (APTs)