Regulators stopped accepting point-in-time cybersecurity years ago. What they expect now is traceability and continuous justification across the product lifecycle, and that is a systems problem, not a testing problem. So ELTON is built as one connected loop: model the product, discover what is wrong with it, verify what is actually exploitable, prove out the decisions, and keep all of it current as the world changes.
Everything starts with architecture. ELTON builds a digital twin of the product from the QMS documentation you already maintain: components, data flows, trust boundaries, assets, and security controls, stored as a graph rather than a drawing. Every finding that arrives later attaches to a specific component and a specific path instead of floating in a spreadsheet.
The twin is also the artifact reviewers probe first, because it demonstrates you understand your own attack surface. FDA premarket guidance and IEC 81001-5-1 both push in exactly this direction.
With the twin in place, ELTON brings in vulnerability data across the whole product: firmware, software, web, mobile, and network. Penetration testing results, SAST and DAST findings, SBOM-derived CVEs, configuration weaknesses, and live intelligence feeds all land in one data model, deduplicated and linked to the components they touch.
Correlation is the point. Three tools reporting the same flaw three different ways is noise. One finding tied to one component on one path is something you can reason about, and something you can defend.
It also keeps testing continuous rather than ceremonial. Findings from an ongoing pentest cadence land in the same model as automated results, so the picture of the product sharpens all year instead of resetting with every new engagement.
Discovery tells you a vulnerability exists. It cannot tell you whether the exploit works on your device as shipped, because exploitability is a runtime question. So ELTON answers it against the real product: whether the component is reachable from an actual attack surface, whether preconditions hold in the shipped configuration, whether compensating controls break the path, and whether lower-severity findings chain into a credible attack.
Ratings are then adjusted using the CVSS rubric recognized under FDA's MDDT program, so severity reflects the product in front of you rather than a worst-case abstraction. Not inflated and not suppressed, just grounded in how the device actually behaves.
Every finding ends in a disposition a reviewer can audit: fix it, mitigate it, monitor it, or document why it requires nothing at all, with the rationale traced back into the twin. VEX output carries the same conclusions in machine-readable form. Reports stay living documents that reflect current state instead of aging into fiction the day after export.
This stage is also where teams weigh trade-offs. What-if analysis shows how a proposed package of fixes would shift overall posture before anyone commits an engineering quarter to it, which tends to kill the reflex of patching for optics.
New CVEs land weekly and releases keep shipping. ELTON re-scores affected components whenever context changes, tracked per commercial release, so the version under FDA review and the version in the field each hold their own current, defensible record. That is what postmarket expectations actually describe: ongoing evaluation you can show, not an annual scan you can schedule.
The loop matters more than any single stage in it. A pentest without a model produces findings nobody can place. A model without verification produces theory. Wire the stages together and vulnerability management stops being an annual event and becomes a property of the product itself. That was the whole idea.
Start with one device. We build the twin from documentation your quality system already produces, run AI discovery remotely, and show you the graph: the handful to fix, and the evidence for everything else.
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ELTON runs as one connected loop with five stages: model the product as a digital twin, discover what is wrong with it, verify what is actually exploitable, prove out every decision with an auditable disposition, and monitor so all of it stays current. Monitoring feeds the model again, which is what closes the loop.
The digital twin is built from the QMS documentation a manufacturer already maintains: components, data flows, trust boundaries, assets and security controls, stored as a graph rather than a drawing. Every finding that arrives later attaches to a specific component and a specific path instead of floating in a spreadsheet.
Three tools reporting the same flaw three different ways is noise. Penetration test results, SAST and DAST findings, SBOM-derived CVEs, configuration weaknesses and live intelligence feeds land in one data model, deduplicated and linked to the components they touch. One finding tied to one component on one path is something you can defend.
Discovery tells you a vulnerability exists. It cannot tell you whether the exploit works on the device as shipped, because exploitability is a runtime question. Verification asks whether the component is reachable from an actual attack surface, whether preconditions hold in the shipped configuration, and whether compensating controls break the path.
Affected components are re-scored whenever context changes, tracked per commercial release, so the version under FDA review and the version in the field each hold their own current record. That is what postmarket expectations describe: ongoing evaluation you can show, not an annual scan you can schedule.