VaultMorph SecureGraph AI
VaultMorph SecureGraph AI
SecureGraph AI is a GUI-first desktop security platform. It runs SAST, taint analysis, SCA, secrets scanning, IaC, container, DAST, API threat modeling, AI/MCP security, and cryptographic-inventory (CBOM) checks — then correlates every finding onto one Security Graph, so you see what's actually reachable and exploitable instead of a long undifferentiated list.
Every scanner produces a pile of findings. The hard part isn't running more scanners — it's knowing which findings are duplicates, which are connected, and which are actually reachable in your real application.
An active check you run on demand or in CI. It executes one or more engines against your codebase or artifact and produces raw findings for that run — your evidence that a library, endpoint, config file, or image has a known weakness.
What SecureGraph AI builds from your scans over time: the Security Graph, Correlation, Attack Paths, and a risk score that tracks how your posture changes scan over scan — not a one-off snapshot that goes stale the moment you close the report.
Every engine's output rolls into one cross-cutting Findings view and into the Security Graph for correlation — you don't review ten separate tools' reports by hand.
Code-level flaws — injection, unsafe deserialization, hardcoded logic bugs, and more — via rule-based pattern matching.
Data-flow tracing from untrusted input to a dangerous operation across function and file boundaries — catches what plain pattern-matching misses. Fully built in, no external tool required.
Known-vulnerable open-source dependencies, checked by version against CVE data.
Hardcoded credentials, API keys, and tokens committed to the repo — several providers support live validation.
Misconfigurations in Terraform, CloudFormation, Kubernetes manifests, and similar IaC.
Base-image vulnerabilities and registry drift for your container images.
Runtime, black-box testing against a live, consent-gated target — typically reviewer-initiated, not an everyday pre-commit check.
API surface mapping and threat modeling for your services.
Security posture of AI/LLM integrations and the MCP (Model Context Protocol) servers your code talks to.
Every cryptographic asset in use — algorithms, key sizes, padding modes — and whether it's weak, deprecated, or strong.
Once you have scans across multiple engines, three views build on top of them — and keep updating as you scan again.
A navigable graph of your application's components, data flows, and findings, with neighbor-relationship exploration.
Groups findings that share a root cause or are duplicates reported by different engines, so you review one grouped issue instead of five near-identical ones.
Chains correlated findings into candidate exploit paths, so you see which combinations of individually low/medium findings actually add up to something exploitable.
The same underlying graph and findings, shown at the altitude each audience actually needs.
| Dashboard | Audience | What it shows |
|---|---|---|
| Executive | Leadership | Overall posture, trend, top risks in plain language |
| Security | Security team | Aggregate findings, coverage, and risk across the portfolio |
| Developer | Engineers | Per-repo findings, scan history, and remediation status |
| AI Governance | Security & compliance | AI/LLM integration posture, MCP server risk, AI Context Firewall activity |
SecureGraph AI Desktop Community is the current edition: every scanner, every export, every finding, and every base report is available whether or not you register. Free email registration additionally unlocks the governance-level dashboards below — no expiry, no credit card.
Free Community edition. No credit card. Registration is optional and only unlocks governance dashboards.