Evidence-Based AppSec That Proves the Exploit.

DefendLab is an AI-native application security platform built to close the gap between what traditional scanners report and what actually gets exploited. Conventional SAST and DAST tools are repo-bound and pattern-matching — they surface false positives at scale while consistently missing business logic vulnerabilities like broken access control, IDORs, and authentication bypasses. DefendLab deploys an agentic layer that reasons about code the way a security engineer would, pulling cross-repository context to surface the class of flaws that static analysis structurally cannot reach.

The platform operates in three stages integrated directly into CI/CD pipelines via native GitHub and GitLab connectors. AI-powered scans analyze code across repository boundaries to detect OWASP Top 10 weaknesses alongside complex logic flaws. A runtime exploitability validation layer then separates theoretical findings from confirmed exploit chains — distinguishing a potential SQLi from one that actually executes. The final stage delivers AI-assisted remediation with evidence attached, giving developers a proof of exploit alongside the fix guidance rather than an unexplained severity score.

DefendLab's distinguishing focus is consensus and precision: a mixture-of-agents architecture reaches agreement before surfacing a finding, which the company claims cuts alert noise by 90 percent while delivering three times the signal of conventional scanners. The platform is designed for engineering-heavy product security teams at companies where broken access control and IDOR vulnerabilities represent the dominant share of high-severity findings and where pentest coverage alone is too slow and too infrequent to match development velocity.