This is about putting guardrails around code written by AI assistants (like GitHub Copilot or ChatGPT) so that insecure code doesn’t sneak into your products. Think of it as a security scanner and policy engine that constantly checks and enforces rules on everything AI is allowed to contribute to your software.
AI code assistants can quickly introduce security vulnerabilities, license violations, and compliance issues into the codebase at scale. Traditional AppSec processes are not designed for the speed and volume of AI-generated changes, creating new risks of bugs, data leaks, and regulatory non-compliance.
Deep integration into CI/CD pipelines and DevOps workflows, combined with security policy engines and specialized detection rules for AI-generated code, can create a sticky, organization-specific control plane that is hard to replace once embedded.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Scanning large monorepos and high-frequency AI-assisted commits in CI/CD can create performance and cost bottlenecks, especially if multiple checks (SAST, secrets, license, policy) run on every change.
Early Adopters
Focuses specifically on managing and securing AI-generated code and its lifecycle within modern CI/CD pipelines, rather than just generic static code analysis; positions itself as an application security posture management layer tuned for GenAI-assisted development.