Built into every phase
From the first line of AI-generated code to live production traffic, Bright tests it, fixes it, and proves it's secure, so nothing ships unverified.
Under 3% false positives on findings validated against your real environment.
5 benefits of Bright
Trusted by security teams at companies like
Built for every phase
From the first line of code to production traffic — here's exactly what runs where.
Why it matters
Software delivery is moving through four eras, and the tools that secured yesterday's SDLC won't hold up in the next one.
Copilots & developers are pilots.
Agents handle multi-file changes; developers are air traffic controllers.
Intent-driven system generation & self-healing code. Developers are designers.
Software evolves based on business outcomes.
AI generates thousands of lines of code per hour, flooding pipelines with vulnerabilities.
AI coding solutions and static scanners don't perform exploitability or reachability analysis, resulting in >60% false positives.
Chasing false positives wastes developer time and explodes compute and AI token costs.
Without validation, AI "fixes" don't actually fix vulns — and create regressions and new flaws.
The AI-native security layer
Verified exploitability, machine-readable signals, and validated remediation — built for AI agents, not just humans.
Filters signal from noise, with under 3% false positives.
AI-native benefitTests for reachability & exploitability, preventing AI agents from fixing "noise."
Structured, proven exploitability data to guide AI agents.
AI-native benefitEnables agents to act safely at machine speed.
Tests live behavior and exploit paths in real time.
AI-native benefitSecures systems that never stop changing.
Verifies AI-generated fixes before deployment.
AI-native benefitEliminates incomplete patches, regressions, and new vulnerabilities.
A closed loop that lets AI agents generate, fix, and ship code without a human validating every step.
AI creates a new feature or service.
STAR finds vulnerabilities and proves exploitability.
AI agents fix issues using contextual guidance from STAR.
STAR validates the fix is effective and safe to deploy.
Policy engines approve deployment based on verifiable evidence.
Proof it works
In the rapidly-evolving world of software development, ensuring a high level of security is a critical priority for businesses.
LivCor had one week to onboard a new application security solution, scan a key application, remediate findings, and push to production.
In the fast-paced financial services industry, delivering secure and feature-rich digital products quickly is paramount.
Resources
Dynamic Application Security Testing has been part of AppSec for a long time. What's changed is where it has to…
Loris Gutić · February 4, 2026Most teams didn't ignore security on purpose. For years, it just made sense to treat it as a final step…
Loris Gutić · January 16, 2026AI-generated code is basically the holy grail of developer tools of this decade. Think…
Amanda McCarvill · June 10, 2024Bright tests AI-generated code against the running application to confirm which vulnerabilities are actually exploitable. Bright STAR finds the vulnerability, proves it's exploitable, gives AI agents the context to fix it, and then verifies the fix is effective and safe to deploy.
Yes. Bright tests the application and API behavior those features expose, then confirms which issues are actually exploitable.
Bright STAR finds the vulnerability, proves it's exploitable, gives AI agents the context to fix it, and then verifies the fix is effective and safe to deploy. Developers keep review and approval control.
Static scanners and AI review tools look at code patterns. Bright validates exploitability against the running application, so teams only remediate what is real.
Bright plugs into IDEs, GitHub, CI/CD, and MCP so testing, remediation context, and verification happen where developers and agents already work.
