Healthcare Security Challenges Leveraging Generative AI to Transform Cybersecurity

Abstract

Generative AI technologies, such as GANs and Transformer-based models, are transforming healthcare and cybersecurity. In healthcare, they improve medical imaging, diagnostics, and personalized treatments, enhancing patient outcomes and operational efficiency. In cybersecurity, generative AI strengthens defenses through real-time threat detection, anomaly identification, and synthetic data generation for secure testing, tackling modern cyber threats. Both fields, however, face challenges in data quality, ethics, transparency, and regulation. Addressing these requires domain-specific frameworks like the Technology Acceptance Model (TAM) in healthcare and Zero Trust Architecture (ZTA) in cybersecurity. This chapter explores generative AI's impact, highlighting challenges, tailored solutions, and strategic frameworks to ensure ethical and operational effectiveness. As AI evolves, it stands as a cornerstone for progress in both fields, balancing innovation with responsibility.

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