AI-Driven Self-Healing IT Systems: Automating Incident Detection and Resolution in Cloud Environments

Abstract

Managing cloud infrastructure is becoming more challenging as systems grow in size and complexity. Traditional IT management methods that rely on manual intervention are becoming insufficient in maintaining system reliability, security, and performance. AI-driven self-healing IT systems address these challenges by leveraging artificial intelligence (AI), machine learning (ML), and automation to detect, diagnose, and resolve issues in real-time. These systems continuously monitor infrastructure, analyze system performance, and detect anomalies before they escalate, enabling automated corrective actions such as restarting services, reallocating resources, or applying security patches. This article presents a structured methodology for implementing AI-driven self-healing systems, focusing on real-time monitoring, automated incident detection, and intelligent resolution strategies. By integrating machine learning, these systems continuously learn from past incidents, improving their decision-making over time. The benefits include minimized downtime, enhanced operational efficiency, reduced human intervention, and optimized resource management. However, challenges such as model accuracy, integration with legacy systems, and balancing automation with manual control remain key considerations. As businesses increasingly adopt AI-powered solutions to manage IT infrastructure, self-healing systems are emerging as a game-changer in cloud computing, paving the way for more resilient and adaptive environments. This study highlights their transformative potential and the future of autonomous cloud operations.