Gurucul Data Optimizer is a data pipeline management platform designed to reduce SIEM and storage costs while improving security data quality. The platform collects data from multiple sources in various formats and routes it to different destinations including SIEMs, data lakes, and low-cost storage. The product provides granular filtering and deduplication capabilities to reduce data volume before ingestion, with claims of 40% cost reduction initially and up to 87% with tuning. It normalizes and enriches log data using machine learning models, adding context from threat intelligence and identity linking to reduce false positives by up to 70%. The platform offers low-code/no-code pipelines with pre-built connectors for rapid onboarding of new data sources. It includes a universal collector that centralizes data from any source and format, with the ability to request custom connectors. Data can be filtered, downsampled, and routed based on business needs or security context. The solution maintains full-fidelity logs in low-cost storage with replay capabilities for compliance and forensic investigations. It provides federated search across all data sources and supports audit-ready archiving. The platform can be deployed standalone or integrated with analytics platforms. Pipeline maintenance is reduced through automation, eliminating manual overhead. The system supports role-based routing to deliver tailored data to SOC, IT, or compliance teams. It correlates activity across sources to provide context for investigations.