AI and ML Enabled Secure Healthcare Information Infrastructure: A Next-Generation Threat Prevention Model

Abstract

The growing use of interconnected and digital systems in clinical settings has increased the necessity of smart and robust protection systems that can assume extremely rigid privacy and reliability requirements. This paper presents the GuardianMesh: Anomaly-Resilient Federated Orchestration (GM-ARFO) a new AI-based threat prevention model that can be used to provide security to the world of distributed healthcare information ecosystems. The method proposed will allows collaborative intelligence between heterogeneous medical nodes and does not present sensitive patient information or centralised control. GuardianMesh (GM) works by using local clinical and system cues to create compact privacy preserving representations in the edge and then a detection of anomalous behaviors is possible early on. These depictions are jointly trained in an effective federated orchestration system that is resilient to adversarial manipulation and communication inefficiently. A dec-layer adjudication layer is what is used to package distributed evidence of anomalies to facilitate swift and automatic response procedures with have minimum impact to clinical processes. Moreover, adaptive monitoring adapts to behavior drift and the changing attack plans all the time, ensuring the reliability of detection over a long period. Thorough tests in various conditions of operation and adversary show that GM-ARFO has a high level of detection, low false alarms, lower response time in addition to maintaining data confidentiality. The findings support the fact that the suggested GuardianMesh framework offers a scalable, future-restaurant, and privacy-aware platform of ensuring the safety of next-generation healthcare information infrastructures. The suggested method attains an overall detection accuracy of 96.8%, indicating a highly dependable identification of anomalous behaviours in remote healthcare systems.