AI/ML security for imaging AI and SaMD - model integrity, PCCP-aligned change control, training-data governance, and adversarial-input testing.
Last reviewed March 2026 · Reviewed against the FDA Feb 3, 2026 final premarket cybersecurity guidance.
AI/ML security for imaging AI and SaMD is its own discipline because the model is the product, and the security boundaries are different from traditional software: training data lineage, model artifact integrity, inference-path adversarial robustness, and the Predetermined Change Control Plan (PCCP) as a security boundary. Our service for this segment covers all four, aligned to FDA's AI/ML PCCP guidance and the 2026 final premarket cybersecurity guidance.
We document model provenance and training-data lineage at the level reviewers now expect: dataset source, dedup and PHI-handling controls, train/test split integrity, and the audit trail that proves the deployed model came from the training data on file. We treat model artifacts as SBOM components with hash-pinned versioning and signed distribution. We test the inference path for clinically-plausible adversarial inputs (not academic perturbations - inputs in the distribution your device will actually see), confidence-suppression paths, and metadata-driven shortcuts. Most importantly, we security-model the PCCP itself: which model updates are allowed under the PCCP envelope, who authorizes them, and what stops an unauthorized model from being shipped under cover of an approved PCCP change. This is the question reviewers are starting to ask, and most submissions don't have an answer.
The imaging & ai/samd system, from the outermost cloud and clinician surfaces down to the device itself. Highlighted layers are exercised by this ai/ml medical device security.
Layers shown outermost (top) to innermost (bottom). Dashed rows are part of the surrounding system but out of scope for this view.
Four phases, fixed fee, scoped to imaging & ai/samd architecture from kickoff onward.
Architecture review, attack-surface walkthrough, and threat-model alignment with your team. Written scope in 24 hours.
Every STRIDE entry in your threat model is matched to a planned test case so reviewers see one-to-one coverage.
Device, cloud, mobile, BLE/RF, and OTA channels exercised in parallel by senior engineers - not a single web-app scan.
eSTAR-format report with findings, CVSS, remediation, and retests until every finding with uncontrolled risk is closed.
The patterns we hit in this segment, this service, again and again.
PCCP defines clinical performance bounds. Security envelope (who can update, what's signed, what's audited) absent. Reviewer asks.
Model files distributed via container build. No signature, no manifest hash. Substitution detectable only by training-data audit.
Data ingest pipeline manual; lineage reconstructable but not on file. Reviewer asks for documented chain of custody.
Specific input metadata combinations cause max-confidence outputs without inference. Documented as 'edge case' in model card; security implications not addressed.
"Blue Goat Cyber takes the burden off our engineers and makes FDA cybersecurity requirements easy to understand. Their expertise and smooth process mean we can focus on our product, not the paperwork. The organized documentation, perfectly formatted for eSTAR, saves us countless hours."
The same deliverables the parent AI/ML Medical Device Security service ships with - tuned to your imaging & ai/samd architecture.
Reviewer-format documents ready to drop straight into the cybersecurity attachments of your submission - no reformatting on your side.
The Imaging & AI/SaMD baseline, plus the call-outs that matter for ai/ml medical device security in this segment.
PCCP is a regulatory and a security boundary. Treat it as both.
Useful framing for training-data governance and model lifecycle controls reviewers are increasingly aligned with.
We scope tightly on purpose. These items are either out-of-scope by design or belong in a separate engagement - we'll tell you up front, not after kickoff.
The questions buyers in this segment actually ask before scoping a ai/ml medical device security engagement.
A practical, ungated buyer's guide for medical device manufacturers evaluating cybersecurity partners, what goes wrong, why it costs you, and what to demand from your next engagement. Aligned to the FDA February 2026 premarket guidance.
The most common high- and critical-severity findings we surface in medical device penetration tests, what each one looks like in the field, and how to fix it before your FDA submission.
A practical, ungated guide to the threat modeling gaps that trigger FDA cybersecurity questions in 510(k), De Novo, and PMA submissions - and exactly how to close them before reviewers find them.
PreSTAR vs eSTAR explained: PreSTAR is the voluntary FDA template for Q-Submissions, eSTAR is required for 510(k) and De Novo. How cybersecurity carries across.
Medical device cybersecurity at each study stage: rodent, large animal, primate, and human trials, and what the FDA expects before your IDE and marketing submission.
What a medical device security advisor does after FDA clearance: keeping the threat model and SBOM current, rating vulnerabilities, and answering hospitals.
Teams in this segment commonly bundle these alongside ai/ml medical device security.
A 30-minute call with a senior engineer who has done this in imaging & ai/samd before - not a sales rep.
