FeatureAutomated Verification

AI Verification Engine

Independent replay validation, confidence scoring, and false-positive elimination with full audit trails. Every finding is automatically verified before it reaches your remediation pipeline — eliminating noise while ensuring nothing is missed.

Verification Pipeline● 3 findings pending
F-001HighReflected XSS in /search0.97
F-002CriticalSSTI in /api/render0.99
F-003MediumOpen redirect in /auth/callback0.82
F-004LowMissing CORS headers0.12
F-005CriticalSQLi in /api/users
85% reduction in false positives · Avg verification time: 12s
85%
False Positive Reduction
99.7%
Verification Accuracy
12s
Avg Verification Time
100%
Finding Traceability
Capabilities

Trust every finding. Eliminate every false positive.

Replay Pipeline

Core

Every finding is independently replayed with the exact same payloads, configuration, and context — ensuring reproducibility and accuracy.

Confidence Scoring

Scoring

Multi-factor scoring weighs evidence strength, attack complexity, environmental factors, and reproducibility to produce a single confidence metric.

False Positive Elimination

Accuracy

Advanced pattern matching and behavioral analysis distinguishes real vulnerabilities from benign anomalies with 99.7% accuracy.

Full Audit Trails

Audit

Complete verification audit trail — who verified what, when, with which evidence, and what decision was made — for compliance and review.

Multi-Angle Verification

Comprehensive

Each finding is verified from multiple angles: payload delivery, response analysis, timing, side-effects, and environmental impact.

Regression Detection

Lifecycle

Track findings across scans — detect regressions, confirm fixes, and automatically re-verify after remediation.

Process

How findings are verified automatically

01

Detect

The scanner identifies a potential vulnerability and captures all contextual evidence — raw requests, responses, tool output, and environmental state at the time of detection.

02

Replay

The verification engine independently replays the finding in an isolated sandbox, using the exact same payloads and conditions, ensuring the result is reproducible.

03

Score

A multi-factor confidence score is calculated based on evidence strength, attack complexity, reproducibility, environmental factors, and historical pattern matching.

04

Confirm

Verified findings enter the remediation pipeline with full evidence packages. Dismissed findings are logged with reasoning for future reference and training.

Verification Pipeline
Detect
Replay
Score
Confirm
Deep Dive

Verification you can rely on

Replay Engine

Independently reproduce every finding with identical payloads, context, and conditions.

Idempotent replay — safe for production
Raw HTTP request/response capture
Timing and latency analysis per attempt
Parallel batch replay for efficiency
Replay history with trend analysis
Export as curl, Python, or Burp Suite

Confidence Scoring

Multi-dimensional scoring that quantifies finding reliability.

Evidence strength (direct vs. indirect)
Attack complexity and exploitability
Reproducibility across multiple attempts
Environmental stability assessment
Historical pattern matching and ML
CVE correlation and known exploit databases

False Positive Analysis

Advanced analysis to distinguish real vulnerabilities from noise.

Behavioral anomaly detection
Contextual response analysis
Timing-based heuristics
Side-effect verification
Multi-angle confirmation
Continuous model training from human feedback

Audit & Compliance

Complete audit trails for every verification decision.

Full verification audit log
Chain of custody for evidence
Role-based access to verification data
Compliance-ready report generation
Tamper-proof evidence storage
SIEM integration via Syslog/CEF
Integrations

Plug into your workflow

JiraServiceNowPagerDutySlackTeamsGitHub IssuesGitLab IssuesSplunkDatadogElasticOpenCVENVDMITRE ATT&CKSARIF
Get Started

Zero false positives. Full confidence.

Eliminate verification debt and trust every finding with automated, AI-powered verification.