Review AI-generated code with the same discipline you expect from human-written code.
GenAI Code Security helps teams inspect AI-assisted code for security weaknesses, embedded secrets, licensing concerns, and policy violations before changes move deeper into the software delivery path.
Code velocity is rising faster than review capacity.
AI coding tools can accelerate delivery, but they also increase the volume of code that needs security, licensing, and quality review. Without better controls, risk simply moves downstream faster.
Engineering teams are under pressure to ship quickly. AppSec teams are under pressure to prove that generated code is being reviewed with appropriate rigor. Both sides need a workflow that reduces exposure without blocking every release.
GenAI Code Security is positioned as a control layer for AI-assisted development: surface issues earlier, route findings into existing review flows, and preserve evidence that generated code was examined before merge or release.
Security weaknesses
Generated code may introduce unsafe patterns, insecure dependencies, or logic that does not match internal standards.
Secret and data exposure
AI-assisted code and prompts can surface credentials, tokens, or sensitive implementation details at the wrong stage of development.
Licensing and policy drift
Teams need confidence that generated code aligns with acceptable licensing and internal review policy.
Add review controls where AI-assisted code already flows.
The goal is not to create a parallel engineering process. The goal is to insert review, policy, and evidence at the points where teams already work.
In-workflow scanning
Inspect AI-assisted output during development rather than waiting for late-stage review or production discovery.
Pre-merge controls
Surface findings in pull request and review checkpoints where developers and reviewers are already making release decisions.
Review evidence
Help AppSec and engineering leaders show that AI-generated code was assessed against policy before release.
Most relevant for teams scaling AI-assisted development under formal review requirements.
This solution typically fits engineering organizations adopting copilots, regulated software teams, and AppSec programs that need a credible answer to AI-generated code governance.
Primary use cases
AI code review, secret detection, insecure pattern identification, licensing checks, and policy gating before merge.
Evaluation questions
Where is AI-assisted code introduced, which repositories are highest risk, and what findings need to block versus inform review?
Deployment posture
Fit depends on source control workflows, CI boundaries, development tooling, and the review evidence required by your organization.
Increase development velocity without lowering review standards.
Tell us where AI-generated code enters your workflow, how review is enforced today, and what evidence engineering and AppSec need to see before release.