Simbian automates what your SOC already knows to do. When your team needs a platform that investigates what it does not yet know, correlates incidents across the stack, and feeds outcomes back into detection, that is where teams start looking for a Simbian alternative.
Executing tasks is not the same as finding the truth.
Automating the response is only useful if you know what you are responding to. Qevlar AI runs the full investigation first, correlating alerts across tools, surfacing related IOCs, and mapping the incident before your team touches a single ticket. 80% of cases closed automatically. The rest handed off with full context.
Ingested data is not validated context
A context lake holds what you put in it. Qevlar AI earns its context, detecting patterns across live investigations and surfacing new items for your team to test and approve before they influence future verdicts. The difference between data ingestion and organizational intelligence.
Transparent reasoning, not a black box verdict
When an LLM drives the investigation, edge cases produce unpredictable results. Qevlar AI uses a graph orchestrator to run every investigation step by step, with LLMs scoped to narrow tasks. Every verdict comes with a full audit trail your analysts can follow and defend.
From reactive firefighting to proactive defense
Simbian automates what your SOC already knows to do. Qevlar AI changes what your SOC knows. Investigation outcomes update detection rules, refine vulnerability priorities, and feed new hunt findings back into the system. Each alert makes the next one easier to close.
Goes beyond alert artifacts to pivot across your entire connected stack
Advanced. Investigations expand beyond the alert boundary: multi-source pivoting, detection of related IOCs, uncovering authentication anomalies, and revealing the full attack scope.
Alert investigation draws from the data available in the alert only. Broader cross-stack pivoting is not part of the investigation model.
Automatically links related alerts into a single incident story
Automatically correlates related malicious activity into a single, prioritized investigation, across any source in your stack.
Each alert is processed independently. Related events are not automatically linked into incident-level stories.
Prevents inconsistent or hallucinated conclusions
Graph orchestration. A proprietary graph-based engine plans the full investigation and adapts dynamically. LLMs handle only narrowly scoped tasks. Same inputs produce the same plan.
Output quality depends on LLM behavior. Variable results are possible under edge-case or unusual inputs.
Every step, every source, every decision visible to analysts
Transparent. Every stage is visible: each observable analyzed, each source queried, each step taken. Complete traceability from raw alert to final verdict.
Investigation steps are logged and visible within the platform.
Builds context to adapt investigations to your environment
Yes, with pre-deployment testing. Qevlar AI accumulates and proactively builds context. Analysts can test the impact of new context before it affects live investigations.
Organizational data is ingested into a context lake. Pre-deployment validation of context items is not supported.
The platform proposes new context based on what it learns
Yes. Suggests new context items based on recurring patterns surfaced across investigations. Routed to your team for review before being applied.
Organizational data is ingested into a context lake. Pre-deployment validation of context items is not supported.
Factors in past alerts, incidents, and ITSM tickets
Yes. Factors in past investigation outcomes and pulls historical tickets directly from ITSM for additional context.
Not available.
Rule tuning and coverage gap identification
Emerging capability. Qevlar AI identifies noisy rules and coverage gaps, with upcoming capabilities to suggest rule tuning and recommend new detections across SIEM, EDR, and cloud stack.
Not available.
Connects security incidents with vulnerability management to prioritize risk
Emerging capability. Connects CVEs to active exploitation and security incidents. Proactively hunts for CVEs and identifies asset owners so teams can act faster.
Not available.
SaaS + BYOC. UK, US, and EU regional hosting available. Your data stays where compliance requires.
SaaS architecture only. No private cloud or on-premises deployment documented.
Rule tuning and coverage gap identification
1,500+ deployments. Adopted by Fortune Global 500 companies and leading MSSPs across 10 countries.
Production evidence at enterprise scale is limited. A small number of public customers are referenced.
This page covers one comparison. The guide covers all of them. We compared every major approach to AI-driven security operations across 18 criteria and 6 dimensions. Free to download.
Your SOC, powered by Qevlar AI
100%
of alerts investigated
across your entire security stack
9%
faster MTTR from triage to containment
80%
less manual work
for SOC analysts
2x
SOC capacity
with the same team
See Qevlar AI in action
Book a 30-minute demo with our team. See how Qevlar AI deeply investigates and makes your defenses stronger with each alert.
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