Qevlar AI is an autonomous AI SOC platform that ingests alerts from security tools, conducts graph‑based investigations, and delivers structured incident reports with verdicts and remediation actions. The system continuously learns from each case, tuning detections, prioritizing vulnerabilities, and enhancing threat‑hunting, so SOC teams can handle alerts faster and focus on high‑value security work.
Funding
$10M 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.
3OFCFounders
Product
Problem
Security Operations Centers (SOCs) are overwhelmed by high‑volume alerts and fragmented detection tools, forcing analysts to spend most of their time on manual triage and investigation rather than proactive defense. This leads to alert fatigue, long mean‑time‑to‑investigate (MTTI), and missed opportunities to strengthen overall security posture.
Solution
Qevlar AI provides an autonomous AI SOC platform that automatically ingests alerts from SIEM, EDR, NDR and other security tools, enriches them with internal and external context, and conducts a deterministic, graph‑based investigation. Within three minutes it delivers a structured incident report with a clear malicious/benign verdict, evidence‑backed reasoning, and recommended remediation actions. The platform continuously learns from each investigation, tuning detections, prioritizing vulnerabilities, and updating threat‑hunting rules, so the SOC becomes more effective over time. Analysts remain in the loop to review and override decisions, but are freed from repetitive data collection, allowing them to focus on high‑value activities such as threat hunting, detection engineering, and strategic risk mitigation.
Target Audience
Primary customers are SOC teams in large enterprises and Managed Security Service Providers (MSSPs) that need to scale alert handling, reduce analyst burnout, and improve detection efficacy.
Features
- Graph‑orchestrated AI engine that defines deterministic investigation steps, ensuring consistent, reproducible results without hallucinations
- Automatic data pull and enrichment from SIEM, XDR, EDR, NDR, CTI and custom business context sources
- Real‑time incident story generation with observable extraction, impact mapping, and remediation recommendations
- Continuous loop closure: false‑positive tuning, containment actions, and vulnerability prioritization feed back into detection and risk models
- Autonomous threat‑hunting module that scans for attacker TTPs, behavioral anomalies, and hidden patterns across historical investigations
- Integration via API or headless mode with existing SOAR, ticketing, and console platforms; supports SaaS or private‑cloud deployment
- Explainable reports with full evidence trail for compliance and auditability