Enhanced Cloud Security Resilience: A Proactive Framework Following the CrowdStrike Incident
Sreejith Sreekandan Nair, Govindarajan Lakshmikanthan · International Journal of Artificial Intelligence Data Science and Machine Learning · 2024
The increasing adoption of cloud-based security solutions has revolutionized enterprise protection strategies but simultaneously introduced critical dependencies on service availability. The CrowdStrike outage of July 2024 served as a stark reminder of how system failures can cascade globally, affecting millions of devices across financial services, healthcare, transportation, and other critical sectors. This paper introduces a novel proactive framework that integrates artificial intelligence-driven cascade failure prediction with quantum-aware architecture to anticipate and mitigate potential security failures before they propagate. Our experimental implementation demonstrated higher accuracy in predicting potential security failures and reduced system recovery time across all test deployments. The framework represents a significant advancement in cloud security resilience by moving from traditional reactive approaches to adaptive, self-healing systems capable of maintaining protection continuity during service disruptions