Autonomous Operational Resilience across AI Guided Cloud Platforms with Proactive Threat Mitigation
DOI:
https://doi.org/10.21590/Keywords:
autonomous resilience, AI-driven systems, cloud platforms, proactive threat mitigation, cyber security, predictive analytics, anomaly detection, cloud orchestration, self-healing systems, operational intelligenceAbstract
In the era of digital transformation, enterprises increasingly rely on cloud platforms to deliver scalable and flexible services. However, the growing complexity of cloud environments introduces significant operational risks, including cyber threats, system failures, and performance disruptions. This research explores the concept of autonomous operational resilience enabled by AI-guided cloud platforms with proactive threat mitigation capabilities. Autonomous resilience refers to the ability of systems to detect, analyze, respond to, and recover from disruptions without human intervention. By integrating Artificial Intelligence (AI) with cloud infrastructure, organizations can achieve continuous monitoring, predictive analytics, and automated response mechanisms. AI models analyze large volumes of real-time and historical data to identify anomalies, predict potential failures, and initiate corrective actions. Cloud platforms provide the scalability and orchestration required to deploy these intelligent systems efficiently. Proactive threat mitigation further enhances resilience by preventing incidents before they occur. This study examines the architecture, tools, and strategies required to implement such systems, along with their impact on operational efficiency and security. While challenges such as data privacy, integration complexity, and trust in AI remain, the findings highlight the transformative potential of AI-driven resilience in ensuring robust and adaptive cloud operations.
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