Rilevera is an AI-driven detection engineering platform designed to continuously validate, optimize, and govern security detection rules across SIEM and other execution platforms. Core Problem Addressed: - Over 40% of detection rules break over time due to schema drift, missing logs, and silent failures - Organizations spend months on manual detection audits - Average functional detection rate is only 37% - Detection engineering is fragmented across tools with no unified control layer Key Capabilities: - Continuous Detection Validation: Validates detection logic, telemetry dependencies, and schema integrity across platforms. Alerts when rules break or data disappears. - AI-Driven Detection Optimization: Analyzes performance data, false positive trends, rule overlap, and logic quality to recommend and push validated improvements back into execution platforms. - Coverage and Gap Analysis: Maps detections and telemetry to MITRE ATT&CK techniques and threat actors to identify blind spots and prioritize new rule development. - Detection Lifecycle Governance: Provides structured workflows for detection design, validation, peer review, and controlled deployment, including version control and change history. Target Users: - Detection Engineers: Seeking automated validation, version control, and confidence that rules function correctly - SOC Managers: Seeking higher signal-to-noise ratio, reduced alert fatigue, and predictable analyst workload - CISOs: Seeking unified visibility into detection coverage, audit readiness, and executive-level reporting The platform integrates telemetry verification, MITRE mapping, red team feedback loops, rule deployment, and coverage reporting into a single workflow.