Qevlar AI vs XBOW
A detailed side-by-side comparison to help you choose the right AI cybersecurity agent for your needs.
Best for AI-powered autonomous security investigations
Qevlar AI
Qevlar AI is built around one central premise: security investigations take too long because they require analysts to manually pivot across dozens of tools, correlate disparate data sources, and const...
AI Models
GPT-4oProprietary graph reasoning modelsCustom ML for IOC correlation
Key Features
- Autonomous multi-hop investigation across identity, network, and endpoint
- Attack timeline reconstruction from initial alert to full scope
- Lateral movement and privilege escalation detection across data sources
- Structured investigation reports with prioritized remediation steps
- Institutional memory of past investigations for pattern recognition
Pricing
Growth — Custom pricing
Enterprise — Custom pricing
Pros
- Autonomous multi-source pivoting eliminates manual investigation steps
- Institutional memory improves accuracy for recurring threat patterns
- Preserves existing tooling investment by acting as an intelligence layer
Cons
- Investigation quality depends heavily on data availability in connected sources
- Pricing not publicly listed, requiring sales engagement for evaluation
Best for autonomous AI penetration testing and vulnerability assessment
XBOW
XBOW is an autonomous penetration testing platform powered by AI agents that simulate the behavior of skilled human attackers. Rather than running a static vulnerability scanner, XBOW's agents reason ...
AI Models
Proprietary offensive security AICustom exploit chaining modelsReinforcement learning agents
Key Features
- Autonomous multi-step exploitation with adaptive attack path planning
- Black-box, grey-box, and authenticated testing modes
- Web application, API, and cloud configuration assessment
- Vulnerability chaining to demonstrate real-world exploitability
- Complete attack chain documentation with reproduction steps
Pricing
Pentest On-Demand — From $4,000/test
Enterprise — Custom
Pros
- Continuous autonomous pen testing catches regressions before production
- Exploit chaining proves real-world impact beyond theoretical CVE listings
- Custom scenario support focuses agents on organization-specific threat models
Cons
- Autonomous exploitation requires careful scope controls to avoid unintended impact
- Does not fully replicate the creative judgment of senior human penetration testers