COGNNA Threat Detection is an AI-driven threat detection platform that provides real-time security monitoring and analysis. The platform uses behavioral analytics, machine learning patterns, and threat intelligence to identify and validate security threats across multiple data sources. The system integrates with SIEM, EDR, cloud, identity, email, and network infrastructure to create a unified data lake for comprehensive threat coverage. It employs a Context Layer that builds an environment-aware map of assets, users, and relationships to enable context-enriched detection. The platform features an AI-led detection engine that performs automated triage and correlation to reduce alert noise. It uses confidence scoring and context enrichment to validate alerts and surface genuine threats while filtering out false positives. The system includes an out-of-the-box detection library that adapts automatically without manual tuning. COGNNA incorporates continuous learning through feedback loops, allowing security teams to fine-tune detections and improve accuracy over time. The platform provides integrated threat intelligence from global, regional, and proprietary sources. It offers cloud-native deployment with streamlined onboarding. The system aims to reduce mean time to detect (MTTD) through real-time detection capabilities and automated validation processes. It addresses alert fatigue by using multi-layered detection with behavioral analysis and AI-led alert processing.