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.

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for AI-enabled medical devices - aligned with FDA's PCCP framework, GMLP, the 2025 AI-Enabled Device Software Functions draft guidance, and AAMI CR34971.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, and IDE submissions - traceable, complete, and aligned with current 524B guidance.

• 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 Medical Devices and SaMDHow EU AI Act Article 15 cybersecurity obligations for high-risk AI compare to the FDA's PCCP framework and Section 524B applied to AI/ML SaMD - model integrity, adversarial robustness, postmarket monitoring.
• FDA PCCP: Predetermined Change Control Plans for AI/ML Medical DevicesHow to author a Predetermined Change Control Plan (PCCP) that clears FDA review - modifications protocol, methods, impact assessment, and cybersecurity coverage under the 2024 final PCCP guidance and the February 2026 premarket cybersecurity guidance.

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 RequirementsAdded by the Consolidated Appropriations Act, 2023, Section 524B 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.
• 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

• Does the FDA Accept AI Pen Testing for Medical Devices?What 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 Device CybersecurityExplore the hidden dangers of AI overfitting in medical devices and its implications for cybersecurity.
• Medical Device AI Model Evasion and Cybersecurity ThreatsExplore the evolving landscape of cybersecurity in healthcare as we delve into the challenges of AI model evasion and the protection of medical devices.
• Medical Device AI Data PoisoningExplore the critical measures needed to protect medical device AI from data poisoning threats.
• Medical Device AI Model InversionExplore the emerging cybersecurity threats posed by AI model inversion in medical devices.
• Medical Device AI Performance DriftExplore the complexities of AI performance drift in medical devices and discover strategies to maintain safety and reliability.
• How to Respond to an FDA Cybersecurity AI RequestReceiving an FDA cybersecurity Additional Information Request (AIR) doesn't mean your submission is dead.

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

Get FDA cleared without the cybersecurity headaches.

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