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.
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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.
Discovery no longer has a barrier to entry. Volume per release keeps climbing while triage capacity stays flat.
Build your own AI analysis that determines which vulnerabilities are actually exploitable against a specific device, not theoretically present in it.
A practical path from manual, point-in-time testing to continuous AI-driven discovery and verification, with a CI/CD feedback loop that stays quality-system compliant.
You will leave with a transition strategy: where to start, what to automate first, and how to keep pace.
A methods session, not a product demo. Every technique covered can be built in-house, regardless of which tools you run today.
Why the cost of finding vulnerabilities collapsed, what the CVE curve does next, and what that means per product, per release.
FDA premarket (524B) and postmarket expectations do not care how many findings there are. Each one still needs a defensible call with evidence behind it.
Architecting automated analysis that tests reachability on the actual device: entry vectors, conditions, and when a finding is a weakness rather than a vulnerability.
Routing confirmed findings to the build pipeline in a quality-system-compliant manner, with the traceability your QMS expects intact.
A practical sequence from manual, point-in-time testing to continuous discovery and verification, sized for teams that cannot pause shipping to rebuild.
Open questions against the architectures in the room: firmware-heavy, SaMD, and connected platforms all run the same playbook differently.
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.
Why remediation is where AI pays off: full context produces a code-level fix that can be pushed and re-tested, instead of a high-level suggestion.
Where to start, what to automate first, and how to keep pace with discovery that no longer has a barrier to entry.
The reason to find everything is not the list. It is that you cannot make an informed decision about a product you only partly understand. Once you know the whole picture, you can rank it honestly, dismiss what is not reachable, and put the effort where it changes something.
Prescriptive fixes were always too expensive to write. A human tester had hours, not days, so you got a category of advice: sanitize input, improve the encryption, harden the interface. True, and almost impossible to act on without redoing the analysis yourself.
Remediation is the part AI is unreasonably good at. It holds the whole context at once: the finding, the surrounding code, the architecture, the constraints. So the output is not a category, it is the change, at the line, in your language and your codebase.
And it does not stop at writing the fix. The same loop can push it, test it, and confirm the finding is actually closed, then start over on the next release. That is the full agentic circle, and it is the part of this shift that pays for itself.
"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 1,000+ vulnerability reports through FDA review.
Recorded September 1, 2026. Forty five minutes plus Q and A, and the slide deck goes with it.
The session covers what happens when AI collapses the cost of vulnerability discovery to nearly zero: why the CVE curve keeps climbing, why every finding still needs an evidenced disposition under FDA premarket 524B and postmarket expectations, how to architect automated analysis that tests reachability on the actual device, and how to route verified findings into CI/CD.
This is a methods session, not a product demo. The material is vendor neutral, and every technique covered can be built in house regardless of which tools you run today. The agenda moves from the discovery trend to the disposition obligation, the analysis, the CI/CD loop, a migration sequence, and Q&A.
Product security and regulatory leaders at medical device manufacturers. The takeaways are aimed at teams who own the triage volume, who have to produce a defensible call on every finding, and who cannot pause shipping to rebuild their process. The recorded Q&A covers firmware heavy products, SaMD, and connected platforms.
Yes, this is the replay. The session aired Tuesday, September 1, 2026 and ran 45 minutes plus Q&A. The recording and the slide deck sit on this page behind one short form, and the page stays unlocked in your browser once you have filled it in. Anyone who registered for the live session can use the same form to get both.
Finding is no longer the bottleneck. AI tools tear through products at a speed humans cannot match, so findings per product per release have outrun manual triage. The harder question is which of them are actually exploitable against a specific device rather than theoretically present in it, and answering that takes device context.