AI has collapsed the cost of vulnerability discovery to nearly zero. Manufacturers now face more findings per product, per release, than any team can triage by hand, each one needing an evidenced disposition under FDA premarket (524B) and postmarket expectations.
This webinar examines the trend and the AI-based response: developing your own AI automated analysis to determine which vulnerabilities are actually exploitable against a specific device rather than theoretically present in it.
We walk through a practical route from manual, point-in-time testing to continuous AI-driven discovery and verification, including how to build a feedback loop that routes verified findings directly into your CI/CD pipeline in a quality-system-compliant manner.
A transition strategy: where to start, what to automate first, and how to keep pace with discovery that no longer has a barrier to entry.
AI tools tear through products at a speed humans can't match. Everyone can find your bugs now, including researchers, regulators, and attackers.
The bottleneck moved. The challenge is no longer finding vulnerabilities. It is knowing which ones are actually exploitable against your product.
How to develop automated analysis that determines real exploitability against a specific device, not theoretical presence in it.
How to route verified findings directly into your pipeline in a quality-system-compliant manner, so proof lands where the code is built.
Why every finding needs a defensible call under FDA 524B and postmarket expectations, and how to produce it at volume.
Where to start, what to automate first, and how to keep pace with discovery that no longer has a barrier to entry.
"AI has collapsed the cost of vulnerability discovery to nearly zero. The manufacturers who prepare now will spend next year shipping. The ones who don't will spend it explaining themselves to regulators."
Jason Sinchak, CEO and co-founder of ELTON Cyber, has spent more than a decade testing medical devices and has taken 600+ vulnerability reports through FDA review.
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.