Qevlar AI
Autonomous AI SOC platform that investigates, responds to and hunts threats across your security stack.
By Qevlar AI · 4.2/5 Value-Position score (estimate)
Positioning guardrails
Best for
- Enterprise SOC teams buried in alert volume that want autonomous investigation and response without adding headcount.
- MSSPs and MDR providers that need consistent, repeatable investigations across many client environments.
- Security leaders who require explainable, auditable AI verdicts rather than black-box triage.
Ideal size: SOC teams of 5+ analysts; enterprise and MSSP scale people · Enterprise or MSSP with an established SIEM/EDR estate and documented SOC procedures
Not for
- Organisations with no existing SIEM, EDR, cloud or ticketing telemetry for Qevlar to investigate across.
- Teams looking for a drag-and-drop SOAR playbook builder rather than an autonomous investigator.
- Businesses without a dedicated security operations function to run the platform.
Value metrics scorecard
Time-to-Value
2-4 weeks
~30 days to first production value
Total Cost of Ownership
On request
Custom enterprise pricing, quoted per deal; demo-led sales with no public list price
Implementation Friction
2/5
Engineering + admin effort required
Value-Position score
out of 5 · model estimate
Full cost breakdown
Mandatory implementation fee
None
Seat tiers
Not published; enterprise-wide licensing negotiated per agreement
Add-on costs
- None
Company & support
Who is behind Qevlar AI, and how your team gets help once it is live.
Market position
Where Qevlar AI sits against its closest alternatives. Pick any two of cost, speed, friction and buyer score, and up to 9 companies to compare.
Quadrant view
Typical annual cost × Time-to-value
The lines cross at the median of the solutions shown, so about half sit on each side of each line.
Companies on the chart 6 / 10
- Qevlar AI
- ConvergePoint
- Admin By Request
- LastPass
- ESET
- Panther
Add or change companies
Up to 10 companies including Qevlar AI. Listed closest first.
Stack fit signal
Compatibility with standard B2B ecosystems.
No supported MCP path today, so it cannot be driven from an AI client.
AI & MCP readiness
What Qevlar AI ships in AI, and what it asks of your ecosystem.
AI features shipped
Vendor states the core investigation runs on a graph orchestrator, not an LLM; LLMs are used only for narrow, bounded tasks such as enrichment and summarisation. Every verdict is transparent, showing steps, queried observables and reasoning, and analysts can override verdicts with context reused in future cases.
Your data & models
- Trains on your data
- Not recorded — ask the vendor
- Runs on
- Not recorded
- AI pricing
- Not recorded
In your ecosystem
- AI connection
- Not supported
- Model key
- Not recorded
- AI usage audit
- Basic visibility
Compliance attestations
* IAPP AIGP certifies individuals, not products. It means named staff hold the credential — not that the platform does.
Bottom line
Qevlar AI is an autonomous AI SOC platform that investigates every alert across a customer's security stack, correlates related activity into incidents, drives containment and tuning, hunts threats continuously and prioritises vulnerabilities. It uses graph-based orchestration, with LLMs reserved for bounded tasks such as enrichment and summarisation, and keeps verdicts explainable and reproducible. It is deployed at over 1,500 organisations and used by Fortune Global 500 enterprises and MSSPs.
Frequently asked questions
How is Qevlar AI different from a SOAR platform?
SOAR executes the static playbooks you have already written and breaks when environments or threats change. Qevlar AI performs the investigative work itself: it enriches alerts, pivots across your stack, correlates related activity into a single incident and adapts its path to what it finds, with no playbooks to maintain. Most teams run it alongside existing SIEM and SOAR tooling rather than replacing it.
Can security leaders trust AI-generated verdicts, and how are hallucinations avoided?
Every verdict is transparent: analysts see each step, each observable queried and the reasoning behind the conclusion. Under the hood Qevlar uses graph-based orchestration, so the same alert is investigated with the same rigor every time. The vendor states an LLM does not run the investigation; LLMs are used only for narrow, bounded tasks such as enrichment and summarisation. Analysts can override any verdict and add context, which Qevlar applies to future cases.
What integrations are required to deploy Qevlar AI?
Qevlar connects to the stack a SOC already uses, via API. Published connectors and categories include SIEM (Splunk, Microsoft Sentinel, Elastic, Cortex XSIAM), EDR/XDR (CrowdStrike, SentinelOne, Trend Micro, Cortex XDR), identity (Okta, Microsoft Entra ID), email (Proofpoint, Mimecast, Microsoft Exchange), ticketing and SOAR (ServiceNow, Jira, Tines, Torq, Swimlane), cloud (Microsoft Azure and Sentinel, Google Cloud and Google SecOps, AWS CloudTrail and GuardDuty) and threat intelligence (VirusTotal, Shodan, AbuseIPDB). Several connectors are marked coming soon.
What does Qevlar AI cost?
Qevlar does not publish list pricing; access is quote-based through a demo request. Pricing is negotiated per enterprise or MSSP agreement, and no self-serve tier or free plan is advertised. Buyers should model annual subscription cost, integration effort and any onboarding services attached to the deal.
What results do existing customers report?
Qevlar's own customer evidence cites a 10x reduction in investigation time, down to about three minutes, 100% of alerts investigated with full depth and context, and 24/7 non-stop investigation. The vendor says the platform is live in production at 1,500 organisations globally and names Fortune Global 500 enterprises and MSSPs including Orange Cyberdefense, Atos, Sodexo and Almond.