
PreSTAR vs eSTAR: What Each FDA Template Is For
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.
Read articleDeep dives on FDA expectations, threat modeling, penetration testing, SDLC, and the standards your team is being asked to meet.
Looking for a quick answer instead? The FDA medical device cybersecurity FAQ covers Section 524B scope, SBOMs, threat models, CR515 and GMLP, deficiencies, and postmarket duties.
Showing 12 of 248 articles · Page 1 of 21

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.
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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.
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What a medical device security advisor does after FDA clearance: keeping the threat model and SBOM current, rating vulnerabilities, and answering hospitals.
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Threat modeling AI-enabled medical devices: how to add the model, training pipeline, and updates to your threat model, with a worked example of a fictional AI dosing advisor.
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AI training data security for medical devices: how to track where data comes from, control who can change it, and show the FDA your training pipeline is protected.
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AI model integrity for medical devices: how to stop model files from being stolen or swapped, sign model updates, and show the FDA the model that runs is the one you validated.
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Hospital security questions for AI medical devices: what reviewers ask about models, patient data, outside AI services, and updates, and how to answer with evidence.
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What investors ask medical device founders about cybersecurity during a raise, what good answers look like, and how to prepare a diligence-ready package.
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Medical device data sovereignty explained: where patient data lives, what HIPAA and GDPR transfer rules require, and how to answer hospital security questionnaires.
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Generative AI medical device security: how prompt injection, data leakage, and unsafe outputs threaten LLM features in devices, and the controls and tests the FDA will look for.
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Cybersecurity hazard analysis for medical devices: how to turn confidentiality, integrity, and availability losses into hazards, harms, and risk controls in your ISO 14971 file.
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DICOM fuzzing for medical devices: which parts of the protocol and file format to fuzz, how to prove a real crash, and what evidence the FDA expects.
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