Scanners tell you what is wrong. The harder part is deciding what to do next. Griffin AI is the part of Safeguard built for that decision.
What it is
The products page calls Griffin AI the reasoning core that turns findings into fix plans. The Griffin page lists a six stage pipeline: scanning, reachability analysis, CVE enrichment with EPSS data, policy validation, automated fix pull requests, and continuous monitoring. It takes in source code, containers, AI models, CI/CD pipelines, third party components and the Gold open source data.
Outputs include SBOMs, compliance reporting, portals and analytics. Griffin is one of several models in the lineup, with Eagle, Lion and Zero.
How to use it
- Ask in the chat first: which of my findings are reachable, and what is the fix?
- Open the fix plan and read each change before approving.
- Check the policy result on the pull request.
- If your rules forbid sending code to a cloud model, use Zero AI mode or bring your own model.
Griffin reasons about reachability, which is whether your code calls the vulnerable function. That filter reduces the list you have to read. It does not remove the need to review a fix.
What to verify
The Griffin page also makes speed and compliance statements. Treat those as vendor claims and ask for evidence that fits your own repositories before you plan around them.
Quick check
What are the first two stages of the Griffin pipeline?
Scanning, then reachability analysis.
