V1I1P12

An AI-Driven Security and Resilience Framework for Cloud-Based Healthcare and Financial Infrastructure

Opeyemi Alao^1*

Abstract

Cloud computing is critical to the operation of healthcare and financial systems; however, it also introduces significant security and resilience challenges due to the sensitivity of data, regulatory requirements, and evolving cyber threats. Many existing cloud security approaches primarily focus on threat detection while overlooking coordinated resilience and recovery mechanisms, leaving critical infrastructure vulnerable to prolonged service disruptions. This study proposes an AI-enabled security and resilience framework that integrates machine-learning–based threat detection, predictive risk analytics, autonomous response mechanisms, and continuous resilience monitoring for cloud-based healthcare and financial environments. Using secondary data analysis and real-world incident case studies, including major ransomware attacks and cloud service outages, the study evaluates the framework’s potential impact on key performance metrics such as Mean Time to Detect (MTTD), Mean Time to Respond (MTTR), fault tolerance, and operational continuity. The findings suggest that AI-enabled approaches can significantly reduce detection and response times, enhance fault tolerance, and improve overall system resilience, thereby mitigating risks to critical U.S. infrastructure. The proposed framework demonstrates how integrating AI-driven security with resilience engineering can support national economic stability, protect essential services, and advance adaptive cloud security practices.

Keywords:

Artificial Intelligence (AI), Cloud Security, Cyber Resilience, Healthcare Information Systems, Financial Infrastructure, Machine Learning, Intrusion Detection Systems, Predictive Analytics, Ransomware Mitigation, Cloud Computing, Critical Infrastructure Protection, Data Privacy, Autonomous Security Response, Zero Trust Architecture, Risk Management