Nebulock is a threat hunting and detection platform that uses agentic AI to identify complex threats in real time. The platform analyzes security data from existing infrastructure to detect lateral movement, insider threats, and credential misuse by establishing behavioral baselines and flagging deviations. The system integrates with EDR, SIEM, and IAM logs to normalize and enrich data while applying behavioral context. Multi-threaded AI agents operate in parallel to hunt for anomalies and suspicious activities across the environment. The platform includes natural language query capabilities that allow security teams to ask questions and receive instant answers without manual querying or SQL knowledge. Nebulock provides real-time feedback loops to reduce alert noise and improve true positive rates. The platform includes simulated attack scenarios to stress test detection rules before deployment. Detection rules continuously learn and refine automatically without manual tuning. The system is designed to reduce investigation time from weeks to minutes per query and decrease dwell time from over 100 days to minutes. It provides audit-ready visibility with real-time tracking of all hunting activities. The platform aims to scale security operations without requiring additional headcount by automating detection of patterns that traditional rule-based systems may miss.