Stop drowning in alerts. Griffin AI autonomously triages, prioritizes, and fixes vulnerabilities across your source code, containers, and AI models — using the same OODA loop methodology trusted by the military.
Your team is drowning in vulnerability alerts they can't keep up with
Security tools generate thousands of alerts. Developers learn to ignore them because most are noise — and real threats slip through.
Investigating each vulnerability, determining exploitability, and creating fixes consumes engineering time that should go to building product.
Security isn't their primary skill. Asking developers to become security experts while shipping features leads to burnout and missed deadlines.
With 70%+ of critical vulnerabilities remaining unpatched, the backlog only grows. Teams can't fix faster than new vulns are discovered.
Griffin AI uses the Observe-Orient-Decide-Act loop to continuously monitor, analyze, and respond to vulnerabilities autonomously.
Automatically generates and submits fixes across source code, container configurations, and AI model dependencies.
Not all vulnerabilities are equal. Griffin AI prioritizes by actual exploitability in your specific environment, not just CVSS scores.
An enterprise software company was averaging 45 days to remediate critical vulnerabilities, with a backlog of over 2,000 unresolved alerts. After deploying Griffin AI, their remediation time dropped to 3 days. The reachability analysis eliminated 80% of false positive alerts, letting the team focus on real threats. The autonomous auto-fix capability now handles the majority of patches without developer intervention.
Four moments where auto-fix is the difference between a routine afternoon and a multi-day incident.
Ninety percent of findings are routine version bumps — same package, minor revision, no behavioural change. Engineers should not be opening these by hand, one repository at a time.
The hurt: hours per week spent on dependency tedium.
A single CVE affects 200 microservices. A platform lead wants one approval to fan the fix out across the fleet, with consistent PR rationale on each one.
The hurt: 200 hand-crafted PRs is a project, not an afternoon.
A candidate zero-day is confirmed in an open-source library you depend on. Rather than patching only your fork, the platform opens an upstream PR with the hypothesis and a proposed patch.
The hurt: every other dependent is still bleeding until upstream merges.
Auto-fix runs the project's own test suite before opening the PR. If the suite fails, no PR is opened and the next-best patch is tried — engineers never see broken fixes.
The hurt: a fix that breaks the build is worse than no fix at all.
A scanner finding, enriched with EPSS, KEV and NVD context, arrives in the auto-fix queue with severity, reachability and ecosystem attached.
Griffin (M 32B for routine fixes, L 70B for complex ones) drafts a patch and writes a cited reasoning trace — root cause, candidate fix, why this version, alternative versions considered.
A child branch is created from main; the manifest or source edit is committed with a structured commit message linking to the finding ID and the reasoning trace.
The project's existing CI workflow runs against the patched branch — unit, integration, build, lint. No new test scaffolding is required.
A pull request is opened with the diff, the cited rationale, the test report and the policy-gate verdict attached as a single review-ready package.
Griffin retries with the next-best patch — a different version, a different transitive pin, or a code-level rewrite — for a configured number of attempts before escalating.
A human reviewer approves once; merge writes back to the finding's audit log and starts the SLA-closure timer.
The same auto-fix surfaces three ways — for the developer at their keyboard, the pipeline, and the leadership review.
Lion (1B) flags the vulnerable line inline, the hover card carries enrichment from NVD, OSV, EPSS, KEV, GHSA, and an "apply suggested fix" button rewrites the manifest in place — diff visible before commit.
The PR comment carries a structured rationale: root cause, candidate fix, reasoning trace, test report, gate verdict — everything the reviewer needs in a single thread, with the diff already in the branch.
Leadership sees percentage of issues auto-remediated this quarter, a time-to-fix histogram by severity, and the count of human-reviewed vs fully-autonomous merges — clear evidence of programme velocity.
Stop wasting engineering time on vulnerability triage. Let Griffin AI handle remediation autonomously.