Sarcouncil Journal of Engineering and Computer Sciences

Abstract: This article presents a comprehensive framework for implementing scalable SIEM architectures in global enterprises, specifically focusing on a high-availability Splunk deployment monitoring over 100,000 endpoints. The architecture addresses critical challenges, including petabyte-scale data processing, sub-second search performance across distributed environments, advanced analytics integration, and business-relevant visualization. Through strategic architectural design incorporating multi-region indexer clusters, optimized data pipelines, and machine learning capabilities, the implementation achieved significant improvements in detection accuracy, query performance, and cost efficiency. The article details the custom clustering architecture spanning four global regions, data optimization techniques that reduced storage requirements by 62%, and machine learning integration that increased true positive detections by 217% while reducing false positives by 64%. Executive reporting frameworks transformed technical security data into business insights, enhancing leadership engagement and strategic decision-making. The implementation realized $2 million in annual cost savings while maintaining 99.99% indexing success rates and sub-second search performance for critical security use cases. This real-world deployment demonstrates that properly engineered security monitoring solutions can simultaneously achieve technical excellence, operational efficiency, and business relevance, providing valuable insights for security architects tasked with similar large-scale implementations.