Security OperationsAI Security
Qevlar AI Autonomous SOC Platform
Investigates every SOC alert from trigger to verdict with a graph AI orchestrator, no playbooks
Qevlar AI Autonomous SOC Platform Overview
What it does
The Qevlar AI Autonomous SOC Platform investigates security operations center (SOC) alerts end to end, taking each alert from trigger to verdict with no playbooks to script and no analyst starting the process. Its core mechanism is a deterministic graph orchestrator that performs all investigative reasoning and follows the same path every time, so decisions stay consistent and auditable, while large language model (LLM) agents are limited to bounded tasks such as enrichment and report writing. Each investigation outcome feeds back into detection engineering, threat hunting, and vulnerability workflows.
How it works
The platform connects through API integrations to existing security information and event management (SIEM), endpoint detection and response (EDR), email, identity, and threat intelligence tools, with deployments typically completed in hours. When an alert fires, the graph orchestrator autonomously pulls and enriches evidence from internal and external sources, reaches a malicious-or-benign verdict, writes a full investigation report, and suggests remediation steps for analysts to validate. Teams use the platform console or run it headless, with verdicts flowing directly into ticketing systems. Customers include Sodexo, GlobalConnect, and the MSSPs Nomios and Almond, which run it across multi-tenant SOC environments.
Credentials and traction
SOC 2 Type II certified. Qevlar AI is recognized in the Gartner Hype Cycle for Security Operations (2026) for the second year running, was named MSP Today Product of the Year in 2025, and won the InCyber Forum Europe Growth Award in 2026. CB Insights ranked it the top performer for Mosaic score improvement in the 2025 SOC AI market. The platform is live in production at 1,500 organizations globally, with customers including Mercedes-Benz, Sodexo, Orange Cyberdefense, ECI, and Atos.
Key Capabilities
mapped to solution categoriesPerforms initial triage of incoming alerts automatically, classifying and prioritizing them to cut tier-1 workload before a human touches the queue.
Identifies and dismisses false-positive alerts with documented rationale, reducing noise reaching human analysts.
Investigates alerts end-to-end from trigger to verdict and closes them out autonomously, so the full volume of raw alerts gets analyzed without resource-constraint concessions.
Automatically gathers and attaches context — threat intelligence, asset and identity data — to alerts during triage and investigation.
Generates investigation summaries and incident reports for analysts and leadership from completed investigation activity.
Recommends the next response actions to take based on investigation findings, up to executing safe predefined closures.
Applies ML classification to incoming alerts to filter false positives, group related events, and route high-confidence detections to analysts, reducing L1 analyst workload.
Suggests the next investigative or containment steps for an alert or incident, with the supporting reasoning, so analysts can confirm and act rather than deciding from raw telemetry alone.
Inserts AI-generated analysis, triage decisions, and enrichment into existing SIEM and SOAR case management workflows rather than requiring analysts to use a separate interface.
Compliance
certificationsIntegrations
compatible toolsImplementation & support
Info last updated on July 25, 2026
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