AI-Based Cybersecurity in Healthcare: A Data-Driven, Governance-Aware Framework for Secure Clinical Systems

Abstract

Artificial Intelligence (AI) is reshaping the healthcare cybersecurity practices by offering capabilities to detect proactively, flexibly, and with data, threats in clinical, administrative, and medical Internet of Things (IoT) infrastructures. Healthcare is among the most desired fields because of the high cost of electronic health records (EHRs), use of outdated infrastructure, fast deployment of cloud computing, and high-regulatory standards. The current paper will provide an elaborate AI-based cybersecurity framework tailored to healthcare settings in particular. Based on real-world breach intelligence data provided by the U.S. Department of Health and Human Services (HHS), the Verizon Data Breach Investigations Report (DBIR), and the IBM X-Force Threat Intelligence Index, we study incidences of attacks and assess AI-enhanced intrusion detections, anomaly detections, and automated response systems. The suggested framework combines machine learning, deep learning, governance controls, and explainable AI to achieve the compliance of the regulations and the trust of the operations [4],[5],[8],[9]. The experimental analysis provides evidence of better detection accuracy of over 95 percent, decreased incident response time, and improved privacy protection. The article adds a new, governance conscious AI cybersecurity architecture applicable to enterprise healthcare system and in accordance with the emerging regulatory, ethical and operational requirements. Recent researches indicate that artificial intelligence used on real-world healthcare data can have a considerable positive impact on clinical decision-making, risk stratification, and operational efficiency.

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