AI/ML medical devices add an attack surface IT and traditional medical-device threat models don't anticipate: adversarial inputs that evade the model, poisoned training data, model-inversion that leaks PHI, and silent performance drift that turns a cleared device into an unsafe one. This hub aggregates our AI/ML cybersecurity services, the FDA's 2025 draft AI guidance and PCCP expectations, GMLP engineering controls, and the threat-class deep-dives our team has published. Use it to scope an AI/ML threat model FDA reviewers will accept, decide what belongs in a PCCP versus a new 510(k), and align your monitoring plan with both cybersecurity and clinical-performance obligations.
The short answer
AI/ML medical devices carry four threat classes traditional models miss: adversarial evasion, training-data poisoning, model inversion and membership inference, and silent performance drift. The FDA expects those to appear in the threat model and the security risk file as patient-harm scenarios, not as data-science concerns. A Predetermined Change Control Plan can cover anticipated model updates, but it must state the security testing that gates each retraining cycle and the monitoring that detects drift in the field.
AI/ML-specific threat modeling, adversarial robustness testing, and submission documentation for AI-enabled medical devices - aligned with FDA's PCCP framework, GMLP, the 2025 AI-Enabled Device Software Functions draft guidance, and AAMI CR34971.
Comprehensive threat modeling per FDA Section V.A.1 - covering supply chain, deployment, environment of use, and decommission risks for the full device system.
Full-service, end-to-end: we deliver 100% of the artifacts FDA reviewers expect for 510(k), De Novo, PMA, PDP, and HDE submissions under §524B, plus IDE applications under 21 CFR 812 and the FDA's February 3, 2026 premarket guidance - traceable, complete, and current.
Hardware, firmware, mobile, and cloud - tested by operators with both red-team and medical-device experience. Reports built for FDA reviewers.
Defined entries from our MedTech Cybersecurity Standards Glossary.
The deficiency letters reviewers most often write on submissions in this topic area. Each links to the full response playbook.
Reviewers say your STRIDE/attack-tree analysis misses interfaces, trust boundaries, or post-market threat surfaces.
Response playbookReviewers find your penetration test scope too narrow, methodology unclear, or testers insufficiently independent.
Response playbookReviewers want test evidence that secure boot, signed updates, and root-of-trust controls function as claimed.
Response playbookYour post-market plan lacks monitoring, patching commitments, customer communications, or end-of-support handling.
Response playbook30-minute strategy session. No cost, no commitment - just answers from people who've shipped 275+ devices supported.
