Nebulock provides an AI-powered platform for autonomous threat hunting across diverse security environments including EDR, IAM, cloud, and network infrastructure. The system uses contextual security analytics and a continuously updated behavioral graph to proactively identify threats that traditional tools miss. This agentic approach operationalizes threat intelligence and refines detection rules automatically, accelerating incident response with transparent reasoning.
Funding
$6M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Security operations teams spend the majority of their time triaging false positives and manually correlating logs, which delays detection of lateral movement, insider threats, and credential misuse. Existing rule sets often remain static, resulting in low coverage and high alert fatigue, while investigative queries across disparate tools can take hours.
Solution
Nebulock delivers an AI‑driven threat‑hunting platform that ingests and normalizes data from EDR, SIEM, and IAM sources in real time. Multi‑threaded agentic AI models continuously learn behavioral baselines and flag deviations indicative of advanced adversary techniques. The system prioritizes high‑fidelity alerts, reducing noise by up to 85 % and enabling analysts to focus on genuine incidents. A natural‑language query interface lets investigators ask questions such as “show me after‑hours admin logins” and receive contextual answers within seconds, eliminating manual SQL‑style searches. Continuous learning automatically refines detection logic and stress‑tests rules against simulated attacks, ensuring coverage stays current without manual tuning.
Target Audience
Primary customers are security operations centers, threat‑hunting teams, and managed security service providers that need real‑time, high‑precision detection across heterogeneous log sources.
Features
- Plug‑and‑play ingestion pipeline that normalizes and enriches logs from EDR, SIEM, and IAM platforms via secure APIs
- Multi‑threaded agentic AI that builds per‑entity behavioral baselines and detects lateral movement, credential theft, and insider anomalies in real time
- Natural‑language hunting engine with contextual understanding, delivering instant query results without requiring query language expertise
- Automated rule generation and continuous‑learning feedback loop that updates detection logic and validates rules against simulated attack scenarios
- High‑fidelity alert prioritization dashboard that reduces false‑positive volume by up to 85 % and surfaces actionable incidents with confidence scores
- End‑to‑end encryption and role‑based access controls for compliance with SOC‑2, ISO 27001, and GDPR requirements
- RESTful API and SDKs for integration with existing security orchestration, automation, and response (SOAR) workflows