Qevlar AI
CB Insights tracks thousands of private companies through its Mosaic score, a composite measure of financial health, market momentum, and investor sentiment. In their 2025 snapshot of the SOC AI market, Qevlar AI recorded the largest one-year improvement of any vendor in the space: +290 points.
The AI SOC space has gotten crowded fast. Most vendors responded to the market opportunity by adding AI layers on top of existing architectures: alert enrichment here, a summary there, maybe an agent that runs a few queries and hands the result back to an analyst.
Qevlar was designed differently. The platform was built from day one for autonomous investigation. At the core is a graph-based orchestration engine that treats every alert as the starting point of an investigation. It expands beyond the initial signal, queries across the full security stack, connects related activity, and produces a verdict with full reasoning attached.
The practical consequence: every alert gets investigated with the same depth and consistency, whether it triggers at 2pm on a Tuesday or 3am on a Saturday, whether it comes from a Fortune 500 enterprise or an MSSP managing hundreds of customer environments simultaneously. The logic doesn't degrade under load.
Most SOC workflows, including most AI-assisted ones, prioritize by severity. High-severity alerts get attention; low-severity alerts pile up. The problem is that sophisticated attacks rarely announce themselves with a high-severity alert.
When those signals aren't investigated, the attack progresses unseen. By the time something triggers that demands attention, the attacker has often been in the environment for weeks.Because Qevlar investigates 100% of alerts, not just the ones that cross an attention threshold, those early indicators get examined. It connects them to related activity across other tools and surfaces patterns that wouldn't be visible in alert-by-alert review.
When investigation is automated and the results are evidence-backed, analysts stop spending the majority of their time on repetitive triage. That capacity shifts to work that actually requires human judgment: complex cases, threat hunting, the kinds of decisions that benefit from experience and context.
There's also a compounding effect for team development. Junior analysts working alongside Qevlar have access to the same structured, evidence-based investigation reports that senior analysts produce after years of experience. The gap in investigation quality between an analyst with six months of experience and one with ten years narrows considerably when both are working from the same documented reasoning.
The operational metrics across Qevlar customers reflect this:
CB Insights' recognition is more than a ranking milestone. It reflects a broader shift in how the market evaluates AI for security operations.
As SOC teams face growing alert volumes, increasingly sophisticated threats, and persistent staffing challenges, incremental automation is no longer enough. Organizations are looking for solutions that can fundamentally change how investigations are conducted, reducing analyst burden while improving detection and response outcomes.
Qevlar's +290-point Mosaic score increase signals growing market confidence in that approach. Rather than assisting analysts with isolated tasks, Qevlar automates the investigation process itself, delivering consistent, evidence-backed verdicts at scale.
