AI SAST — Static Application Security Testing That Reasons | Nurbak
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AI SAST

AI SAST: static analysis that reasons, not just matches

Traditional SAST tools drown you in false positives. Nurbak's AI static application security testing reasons across your codebase and surfaces the bugs that are actually exploitable. Scan a public repo free.

github.com/
Public repos: free, no account. Score in minutes.

Fewer false positives

Instead of flagging every pattern, the agent validates exploitability and ranks the real risks — so triage stops being a chore.

Cross-file reasoning

Catches vulnerabilities that span multiple files and the business logic a rules engine can't follow.

CI/CD friendly

Scan on connect or on every pull request, export to Jira — without changing your workflow.

Private by design

Runs on our own model on ephemeral infra; your code never goes to OpenAI or Anthropic and isn't stored.

How AI SAST works

1

Paste a public repo, or connect GitHub/GitLab read-only.

2

We run our Whitehat model on ephemeral infra.

3

The agent analyzes code and correlates multi-file findings.

4

You get exploitability-ranked results with the fix.

5

Wire it into CI/CD and your Jira on paid plans.

AI SAST FAQ

What is AI SAST?

AI SAST is static application security testing driven by an AI agent that reasons about code instead of only matching patterns, which cuts false positives and finds cross-file, business-logic bugs.

How is AI SAST different from a traditional SAST tool?

A traditional SAST tool matches rules and produces noisy alerts. An AI SAST validates exploitability, correlates findings across files and prioritizes the risks that matter.

Is it free to try?

Yes. Scan a public GitHub repository for free and see the score and summary. Full reports and CI/CD integration are on paid plans.

Does it send my code to a third-party AI?

No. Nurbak runs its own model on ephemeral infrastructure; your code is deleted after the scan and never reaches OpenAI or Anthropic.

Try AI SAST on your repo free

See how much less noise reasoning-based static analysis produces.

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