AI/ML Medical Device Security for Imaging AI & SaMD

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.