SentinelOps: AI-Based System Failure Prediction Using Application Logs SentinelOps (AI-SFPL)

Modern web applications generate massive volumes of application logs that contain valuable information about system behavior, server performance, and error events. Traditional monitoring tools rely on rule-based alerts or manual log analysis, which detect failures only after they occur. This reactive monitoring approach results in increased downtime, reduced reliability, and delayed response to system anomalies. This paper proposes SentinelOps, an Artificial Intelligence based system monitoring platform designed to analyze application logs and predict potential system failures before they occur. The system integrates a web-based log generation environment, a machine learning powered log analysis engine, and a visualization dashboard for monitoring system health. Log data generated from the web application is processed using Python and Pandas, while machine learning algorithms such as Isolation Forest and Random Forest are used to detect anomalies and predict failure risk. The processed insights are visualized through interactive dashboards that display system performance metrics, error frequency, and predicted failure probabilities. Experimental evaluation demonstrates that the proposed system can successfully detect abnormal behavior patterns in application logs and recommend corrective actions to administrators. SentinelOps provides an intelligent and proactive monitoring solution that enhances system reliability and reduces operational downtime.