Balancing Detection Speed and Resource Efficiency in Healthcare Vulnerability Scanning

Vulnerable systems are a prime target for cyberattacks in healthcare organizations. This paper explores how automatic vulnerability scanning, triggered by network changes through Python scripting, can significantly reduce the window of opportunity for attackers. We delve into the crucial yet challenging task of finding the optimal scan frequency, balancing faster detection of vulnerabilities with minimized resource consumption on healthcare networks. A risk-based framework is presented to guide this selection process. Furthermore, we address the issue of false positives in Network Traffic Analysis (NTA) and propose mitigation strategies to enhance the accuracy of threat detection. By implementing these recommendations, healthcare organizations can establish a proactive approach to network security, safeguarding sensitive patient data and ensuring the continuity of critical healthcare services.