Sarcouncil Journal of Multidisciplinary

Abstract: Contextual anomaly detection emerges as a transformative approach for enhancing Site Reliability Engineering practices by bridging the gap between technical monitoring and business outcomes. Traditional SRE models focused primarily on infrastructure health metrics often fail to capture actual business impacts when systems underperform. By integrating machine learning algorithms with business KPIs, organizations can establish sophisticated baseline behaviors that account for temporal patterns, regional variations, and other contextual factors. This integration enables more accurate differentiation between normal business fluctuations and genuine anomalies requiring intervention. The evolution toward business-aligned reliability frameworks represents a fundamental shift in how digital platforms approach service reliability, prioritizing incidents based on business impact rather than purely technical severity. Through adaptive alerting systems, cloud-native architectures, and automated remediation workflows, organizations can significantly improve incident detection and response while demonstrating tangible business value through protected revenue and enhanced customer experiences.