AI-Driven Cyber Security for Safeguarding Critical Infrastructure and Patient Data

Abstract

The growing digitization of crucial infrastructure, especially in areas like health care, has put these sectors at risk of complex cyberattacks. Managing a patient's protected health information (PHI) and operational systems of health care facilities is highly sensitive; breaches can result in data theft, service interruptions, and even endanger the safety of patients. This paper presents the use of AI cybersecurity solutions for the protection of vital infrastructure and patient data with advanced anomaly detection, intrusion prevention, and threat intelligence systems. AI algorithms can analyze large amounts of information in real time and identify new threats while adapting defenses automatically. The study covers central applications of AI: machine learning-enhanced intrusion detection systems, endpoint protection, and behavior-based mitigation of cyber threats. It also tackles issues of model accuracy - false positives, privacy, and model size. Further, the paper presents an integrated cyberspace security approach designed specifically for the healthcare systems with the goals of minimizing exploitable assets and maximizing sensitive data security. The core directions are aimed at building transparent AI models and employing blockchain technology for improved assurance of data integrity.

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