Use Case

Eliminate Vulnerability Exposure

Your software dependencies run 60+ levels deep. Critical vulnerabilities hide where no scanner can reach — until now. Griffin AI scans 100 levels deep and autonomously remediates threats before they become breaches.

100
Dependency Levels Scanned
80%
Fewer False Positives
92%
Faster Remediation
3 Days
Avg. Time to Fix

The Hidden Crisis

Most organizations don't know the full extent of their vulnerability exposure

01

Dependencies Run 60+ Levels Deep

Transitive dependencies create a hidden web of code you didn't write, didn't review, and can't control. Most scanners only check 5-10 levels.

02

70%+ Critical Vulns Never Get Fixed

Development teams are overwhelmed. They can't fix what they didn't build, and they can't prioritize what they can't see.

03

Code Quality Varies Wildly

Open source maintainers range from world-class engineers to hobbyists. You inherit all of their security decisions.

04

False Positives Waste Engineering Time

Without reachability analysis, teams waste weeks chasing vulnerabilities that can never actually be exploited in their codebase.

How Safeguard Solves This

Deep Scanning. Autonomous Remediation.

100 Levels Deep

Griffin AI scans 100 dependency levels — 40+ more than any competitor. No vulnerability hides from Safeguard.

Transitive dependency mapping
Binary composition analysis
Source-to-deploy traceability

Reachability Analysis

Not every vulnerability is exploitable. Our reachability engine determines which vulns can actually be reached in your specific codebase.

80% reduction in false positives
Actual call-path analysis
Context-aware prioritization

Autonomous Remediation

Griffin AI doesn't just find vulnerabilities — it fixes them. Automated patches, pull requests, and container rebuilds.

Auto-generated fix PRs
Container image rebuilds
Zero-day response automation
Real Result

Healthcare Customer Prevents $25M Ransomware Attack

A major healthcare provider discovered a critical vulnerability buried 87 dependency levels deep — far beyond what their previous scanner could detect. Safeguard's Griffin AI identified the threat, confirmed it was reachable, and generated an automated fix within hours. The vulnerability was the same exploit vector used in a $25M ransomware attack on a competitor that same quarter.

87
Levels Deep
$25M
Attack Prevented
45→3
Days to Remediate

Where this use case bites in real life

Four moments where exposure stops being a dashboard number and starts being a deadline.

01

Log4Shell-class disclosure at 6pm Friday

A new RCE drops just as the team logs off. Leadership wants to know where you're exposed across 4,000 services before customers and journalists wake up.

The hurt: you need an answer in minutes, not Monday.

02

Quarterly SOC 2 evidence

The auditor wants proof that every Critical finding was triaged within SLA last quarter — ticket numbers, owners, decision rationale, timestamps. Not a tool screenshot.

The hurt: reconstructing triage history by hand is a week of work.

03

M&A diligence

A buyer's security team wants the acquired company's full CVE exposure with reachability evidence — not a CSV from a scanner, an actual risk view they can defend to their board.

The hurt: a raw vulnerability list will not close the deal.

04

Pre-release go / no-go

Launch is in 12 hours. Marketing has booked press. Security needs a verdict on every blocker still in the build, with clear reasoning for any waivers.

The hurt: "we're still scanning" is not an acceptable answer at T-minus-12.

The Flow

How Safeguard handles it, step by step

01

Scan trigger

SCM webhook on push, scheduled sweep, or manual run from console — every repo enters the queue with a signed event.

02

11 scanners run in parallel

SCA, SAST, IaC, secrets, container, license, dependency confusion and four more — all execute concurrently against the same commit.

03

Eagle (13B) dedup and cluster

Findings from overlapping scanners are merged into single issues with combined evidence — no double-counting in the queue.

04

Reachability call-graph pass

Static call graph determines whether the vulnerable symbol is actually invoked from your entrypoints — non-reachable findings are demoted.

05

EPSS + KEV enrichment

Each finding is decorated with EPSS exploit probability, CISA KEV membership, and NVD/OSV/GHSA cross-references.

06

Griffin reasoning on top-N

Griffin (S or M) writes a one-paragraph explanation per top finding — root cause, blast radius, fix candidates, citations.

07

Write-back + SLA clock

Findings land on the PR, the console, and Jira; the SLA timer starts the moment severity is assigned, per-finding.

What you see, ship, and report

The same finding surfaces three different ways for three different audiences.

In the IDE / CLI

Lion (1B) flags the vulnerable import inline while you type, with hover enrichment from NVD, OSV, EPSS, KEV, GHSA. One-click "apply suggested fix" rewrites the version pin in place.

Inline squiggle on vulnerable symbol
Hover card with CVSS, EPSS, KEV
One-click version bump

In CI / PR

The platform writes a structured PR comment with the gate verdict (pass / fail / waive-needed), the exact failing rule, and — if auto-fix is allowed — a child branch with the proposed patch.

PR comment with reasoning trace
Policy gate verdict + rule ID
Auto-fix branch ready to merge

In the security / exec console

Leadership opens one view: trend lines by severity, SLA breach burndown, top exposed services, and a regulator-ready export button that bundles findings, evidence and remediation actions.

Severity trend by quarter
SLA breach burndown
One-click regulator export

Stop Guessing. Start Securing.

See every vulnerability in your software supply chain — no matter how deep it hides.