Automated Threat Intelligence Integration to Strengthen SHACS for Robust Security in Cloud-Based Healthcare Applications

Abstract

This study presents a novel method for protecting cloud-based medical apps by combining Secure Healthcare Access Control Systems (SHACS) with Automated Threat Intelligence (ATI). By utilizing machine learning algorithms, anomaly detection methods, and real-time threat intelligence, the suggested framework improves cloud healthcare security. While SHACS guarantees safe, dynamic, and context-aware access management for critical healthcare data, ATI integration offers the capacity to proactively identify and address new cyber threats. In addition to fortifying the security architecture, this two-pronged strategy makes it easier to control access in
real time while adjusting to changing security threats. The solution maintains data privacy and compliance while guarding against unauthorised access by guaranteeing compliance with important regulatory requirements like HIPAA and GDPR. Empirical testing revealed that the architecture could withstand complex attacks, as evidenced by its remarkable 94.2% threat detection rate and 95.3% resilience score. Additionally, the framework produced dependable security alerts with a low false-positive rate of only 3.2%. When compared to conventional methods, the suggested alternative provides notable gains in scalability, performance, and operational efficiency. By successfully reducing cybersecurity threats and upholding high system integrity, this integrated solution meets the growing demand for strong security measures in cloud- based healthcare systems. The findings validate the framework's promise as a secure, scalable, and effective solution for the healthcare industry, protecting private patient information in intricate and dynamic cloud environments. Future studies will concentrate on increasing scalability and optimizing resource usage without sacrificing security efficacy