The Necessity of Secure IT Infrastructures in Healthcare Through AI Vulnerability Analysis

This research paper addresses the critical cybersecurity issues of healthcare IT systems. Through the use of artificial intelligence, specifically natural language processing (NLP) and advanced neural networks such as long short-term memory (LSTM), the study closely analyzes vulnerabilities documented in the National Vulnerability Database (NVD). The main objective is to identify patterns in the Common Vulnerabilities and Exposures (CVE) datasets to predict Common Vulnerability Scoring System (CVSS) scores with high accuracy, which is of utmost importance in the healthcare sector where IT failures can have catastrophic consequences. The work aims not only to predict the potential impact on healthcare but also to prioritize the vulnerabilities according to their severity and their relevance to healthcare facilities. The vulnerability descriptions, vectors, and affected software configurations will be evaluated in detail. Through this analytical effort, the study will develop a differentiated framework for the early detection and professional management of IT security threats, aiming to strengthen the cybersecurity defenses of healthcare infrastructures. By contributing to the strategic anticipation and mitigation of risks, the research seeks to improve the resilience of critical healthcare systems and ensure the continuity and integrity of patient care.