VCS-KIAN is a User and Entity Behavior Analytics (UEBA) solution that uses machine learning and real-time big data processing to detect advanced cyberattacks through behavioral profiling and kill-chain analysis. The platform enriches traditional SIEM platforms with behavior-based detection capabilities. The solution addresses challenges related to sophisticated attacks that bypass rule-based detection, insufficient single event anomaly detection, lack of contextual risk scoring, and fragmented monitoring across systems. VCS-KIAN operates through three layers: a data ingestion and profiling layer that normalizes logs from diverse sources and builds behavioral profiles; a behavior analysis and detection layer with three engines (machine learning, advanced rule engine, and risk scoring); and an alerting and integration layer that generates prioritized alerts for SIEM/SOAR systems. The platform automatically profiles users and entities with anomaly detection based on personal history, peer groups, and rare activity patterns. It performs chain-based attack detection to identify multi-step intrusions including webshells, DNS tunneling, and lateral movement. The hybrid detection engine combines rule-based methods, machine learning, and behavior analytics. VCS-KIAN processes and stores up to 10,000 events per second and profiles 10,000 entities continuously for 30+ days. The solution covers over 50% of MITRE ATT&CK techniques and includes a detection scenario builder for analysts to create custom detection logic. Integration with SIEM systems is supported via CEF/LEEF syslog standards.