Shadow Comparator with AI: A Machine Learning Approach for Anomaly Detection in Production Systems

Abstract

Ensuring reliability and stability in production systems is critical for modern enterprises. Traditional anomaly detection techniques often fall short due to their reliance on static thresholds and limited contextual awareness. This paper introduces a novel Shadow Comparator framework enhanced with Artificial Intelligence (AI) to enable robust anomaly detection in live production environments. The system leverages machine learning models to create a parallel shadow environment that mirrors the behavior of the primary system in real time. By comparing the shadow system’s predictions against actual production outputs, the framework identifies deviations indicative of potential anomalies. It incorporates techniques such as time-series forecasting, clustering, and classification to dynamically adapt to system changes without manual intervention. Experimental results from real-world deployment scenarios demonstrate high accuracy in early anomaly detection, reduced false positives, and minimal performance overhead. This approach offers a scalable and intelligent solution for proactive monitoring and self-healing in complex production infrastructures.