Scalable Vulnerability Management in the Internet of Medical Things: An AI-Driven Automated Framework for Threat Mitigation in High-Asset Environments

Abstract

Background: The rapid proliferation of the Internet of Medical Things (IoMT) has expanded the attack surface of healthcare organizations, creating environments with over 100,000 connected assets. Traditional vulnerability management (VM) relies on periodic scanning and manual remediation, which are insufficient for the scale and criticality of modern medical networks.

Methods: This study proposes an AI-driven Automated Framework for Threat Mitigation designed specifically for high-asset environments. Drawing on recent advances in vulnerability management at scale and anomaly detection in time-series data, we developed a hybrid deep learning model utilizing Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) units. The framework was tested in a simulated environment replicating a Tier-1 hospital network with diverse endpoints, ranging from MRI machines to implantable cardiac devices.

RESULTS: The proposed framework demonstrated a statistically significant improvement in threat detection speed compared to legacy systems. Specifically, the automated approach reduced the Mean Time to Remediation (MTTR) by 42% and decreased false positive alerts by 65%. Furthermore, the system maintained 99.99% availability for critical life-support nodes during active threat mitigation protocols.

conclusion: The integration of AI-driven automation into vulnerability management offers a viable path for securing large-scale IoMT environments. However, the transition requires careful consideration of algorithmic interpretability and the ethical implications of automated decision-making in clinical settings.