AI-powered continuous threat hunting platform that proactively searches across EDR, IAM, cloud, network, and SaaS environments using autonomous behavioral agents

Nebulock provides an autonomous threat-detection platform that applies adaptive pattern recognition to identify complex and emerging threats in real time. Built around continuously learning AI agents, the system analyzes activity across the security stack to surface high-fidelity alerts, uncover lateral movement, detect credential abuse, and identify insider anomalies. Its core engine evolves with every interaction, stress-testing and improving detections as it ingests new behavioral signals.

The platform ingests data from EDR, SIEM, IAM, and related log sources, normalizing and enriching telemetry to build contextual understanding of user and system behavior. Nebulock’s agents can be queried in natural language, enabling analysts to ask investigative questions and receive immediate, contextualized answers. The platform augments existing detection infrastructure by applying behavioral context, unifying disparate datasets, and automating the discovery of anomalies that traditional rule-based systems may miss.

Nebulock raised $8.5 million in funding in July 2025. The funds include $6 million in seed funding led by Bain Capital Ventures. Other participants include Decibel, In-Q-Tel, Zetta Venture Partners, Step Function and Aviso Ventures. Unnamed angel investors also participated.