AI/ML Medical Device Cybersecurity
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.
Services
- AI/ML Medical Device Security
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for AI-enabled medical devices - aligned with FDA's PCCP framework, GMLP, the 2025 AI-Enabled Device Software Functions draft guidance, and AAMI CR34971.
- Medical Device Threat Modeling
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 FDA Premarket Cybersecurity
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.
- Medical Device Penetration Testing
Hardware, firmware, mobile, and cloud - tested by operators with both red-team and medical-device experience. Reports built for FDA reviewers.
In-depth guides
- EU AI Act vs FDA AI/ML Cybersecurity for DevicesHow EU AI Act Article 15 obligations compare to the FDA's PCCP framework and Section 524B for AI/ML SaMD.
- FDA PCCP: Predetermined Change Control PlansHow to author a Predetermined Change Control Plan the FDA will accept: modifications protocol, methods, impact assessment, and cybersecurity coverage.
Standards & guidance
Defined entries from our MedTech Cybersecurity Standards Glossary.
- FDA 2026 GuidanceFDA Premarket Cybersecurity Guidance (Feb 3, 2026)The FDA's final premarket cybersecurity guidance, effective February 3, 2026. Defines the seven-section cybersecurity submission format reviewers now enforce at Technical Screening, replacing the 2023 draft. Operationalizes Section 524B of the FD&C Act.
- Section 524BFD&C Act Cyber Device RequirementsSection 524B of the FD&C Act (the statutory partner to 21 CFR 807.81) was added by the Consolidated Appropriations Act, 2023. It gives the FDA explicit authority to require a complete cybersecurity package in every premarket submission for a cyber device, and to refuse submissions that lack one. It works alongside 21 CFR 807.81, which sets the 90-day 510(k) filing floor.
- SPDFSecure Product Development FrameworkA documented framework that shows security activities are integrated across the device lifecycle - not bolted on at the end. Includes secure requirements, threat modeling, secure coding, V&V, vulnerability management, and post-market response.
- ISO 14971Medical Device Risk ManagementThe umbrella risk-management standard for medical devices. Defines hazard identification, risk estimation, risk evaluation, risk control, and residual risk evaluation. Cybersecurity risks must be reconciled here so a security control never silently introduces a safety hazard.
- AAMI TIR57Principles for Medical Device Security - Risk ManagementThe MedTech-specific extension of ISO 14971 for cybersecurity. Defines how to identify cybersecurity assets, threats, and vulnerabilities, then estimate, evaluate, and control the resulting risk.
From the blog
- FDA & AI Pen Testing for Medical DevicesWhat the FDA's Feb 2026 premarket cybersecurity guidance says (and doesn't say) about AI-run penetration testing, where AI helps, where it fails a 524B.
- AI Overfitting in Medical DevicesAI overfitting in medical devices poses cybersecurity threats, leading to misdiagnosis and exploitable vulnerabilities. Learn how to mitigate these risks.
- Medical Device AI Evasion & CybersecurityExplore the evolving landscape of cybersecurity in healthcare as we delve into the challenges of AI model evasion and the protection of medical devices.
- AI Data Poisoning on Medical Devices: Defense Guide (2026)How aI Data Poisoning target medical devices, real-world examples, and the controls FDA expects manufacturers to implement in 2026.
- AI Model Inversion Attacks on Medical DevicesHow aI Model Inversion Attacks target medical devices, real-world examples, and the controls FDA expects manufacturers to implement in 2026.
- Medical Device AI Performance DriftAI performance drift degrades medical device accuracy over time. Learn its impacts, causes, and mitigation strategies to maintain safety and efficacy.
- How to Respond to an FDA CybersecurityReceiving an FDA cybersecurity Additional Information Request (AIR) doesn't mean your submission is dead. Aligned with the FDA's Feb 3, 2026 premarket.
Related FDA deficiencies
The deficiency letters reviewers most often write on submissions in this topic area. Each links to the full response playbook.
- Incomplete Threat Model
Reviewers say your STRIDE/attack-tree analysis misses interfaces, trust boundaries, or post-market threat surfaces.
Response playbook - Insufficient Penetration Testing Evidence
Reviewers find your penetration test scope too narrow, methodology unclear, or testers insufficiently independent.
Response playbook - Insufficient Secure Boot Evidence
Reviewers want test evidence that secure boot, signed updates, and root-of-trust controls function as claimed.
Response playbook - Inadequate Post-Market Cybersecurity Plan
Your post-market plan lacks monitoring, patching commitments, customer communications, or end-of-support handling.
Response playbook
AI/ML Medical Device Cybersecurity - frequently asked questions
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