AI-Driven Forensic Analysis for Cyber Incidents in Healthcare

Abstract

The increasing prevalence of cyber incidents in healthcare has underscored the necessity for effective forensic analysis to ensure data integrity and patient safety. This paper explores the role of artificial intelligence (AI) in enhancing the forensic investigation of cyber incidents within healthcare settings. By leveraging advanced machine learning algorithms and data analytics, AI can significantly improve the speed and accuracy of forensic investigations, enabling faster identification of attack vectors, compromised systems, and data breaches. The study examines various AI techniques, including anomaly detection, natural language processing, and predictive analytics, highlighting their applicability in real-time threat detection and post-incident analysis. Furthermore, the paper discusses the integration of AI-driven forensic tools into existing healthcare IT infrastructures, emphasizing the importance of training and collaboration among cybersecurity professionals. The findings suggest that adopting AI in forensic analysis not only expedites the investigative process but also enhances the overall cybersecurity posture of healthcare organizations. This research aims to provide a comprehensive overview of the transformative potential of AI in forensic analysis, paving the way for more effective responses to cyber threats in the healthcare sector.