Qevlar AI

What Qevlar AI does

Qevlar AI transforms Security Operations Centers (SOCs) into self-improving defense systems. It autonomously investigates every alert across the entire security stack, connecting related activity into single incident stories, mapping blast radii, and moving containment forward according to established procedures. The platform closes the loop on every incident, driving actions like containment, tuning for false positives, and policy follow-up, ensuring each outcome strengthens defenses. Qevlar AI continuously hunts for emerging threats, TTPs, behavioral anomalies, and active exploitation, turning findings into actions and gaps into detection improvements. It compounds institutional knowledge, making investigations faster and decisions more consistent, and connects SOC and vulnerability teams through a shared intelligence layer for prioritizing real risk. The system is designed to provide a security posture that improves with every action, intelligence that compounds, consistently executed strategies, and increased capacity without headcount growth. It integrates with existing tech stacks via API and offers flexible deployment options including SaaS or private cloud.

Key capabilities

• Autonomous alert investigation
• Self-improving defense system
• Continuous threat hunting
• Institutional knowledge compounding
• Shared intelligence layer for SOC and vulnerability teams
• Automated incident reporting
• Remediation action suggestions
• Explainable AI decisions
• Highly adaptable AI
• Privacy-preserving AI
• Flexible deployment (SaaS, private cloud, headless)
• Deterministic graph orchestrator for investigations
• LLM agents for bounded tasks (enrichment, reporting)
• Integration with SIEM/EDR tools
• Integration with email security and CTI tools

Use cases

• Automated alert investigation
• Reducing mean time to resolution (MTTR)
• Eliminating alert backlog
• Widening monitored perimeter
• Minimizing manual security work
• Improving ROI from existing security tools
• Investigating network security alerts
• Streamlining identity alert investigation and remediation
• Investigating cloud security alerts
• Responding to phishing threats faster
• Prioritizing real risk
• Strengthening detection engineering
• Enhancing threat hunting
• Improving vulnerability management
• Reconstructing cloud incidents
• Automating cyber alert responses

AI approach

Qevlar AI uses a combination of generative AI (LLMs) and graph AI. The core of its system is a deterministic graph orchestrator that handles the investigation verdict to avoid hallucinations and ensure consistent results. LLMs are used for bounded tasks such as enrichment and reporting, but not for the final verdict. The AI is designed to be explainable, highly adaptable, and privacy-preserving, pre-trained on non-customer data. It continuously learns and improves to keep up with evolving threats.

Tech named: generative AI, graph AI, LLMs, graph orchestrator

What it says sets it apart

• Security operations become a self-improving defense system
• Investigates every alert across the entire security stack
• Turns each outcome into intelligence that strengthens response, detection engineering, threat hunting, and vulnerability management
• Every case compounds into knowledge for the next, making defense harder to attack over time
• Closes the loop on every incident, driving next actions (containment, tuning, policy follow-up)
• Autonomous hunting for emerging threats, TTPs, behavioral anomalies, and active exploitation
• Adapts to environment, making investigations faster and decisions more consistent
• Shared intelligence layer for SOC and vulnerability teams
• Conclusive investigations and personalized recommendations
• Leverages generative and graph AIs with specific roles
• LLMs are explainable, highly adaptable, and privacy-preserving
• Core is a deterministic graph orchestrator, not LLM-driven verdict
• LLM agents handle only bounded tasks like enrichment and reporting, never the verdict
• Never trains on customer data
• Autonomous SOC that does investigative thinking, not just static playbooks like SOAR
• Reduces average time to investigate alerts to 3 minutes
• Closes up to 80% of tickets automatically
• Provides 24/7 nonstop investigations
• Focuses on improving security posture rather than just processing alerts

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.