Threat Modeling for Clinical ML Systems
Structured analysis of adversarial and failure risks specific to clinical AI.
Clinical ML systems face unique risks — data poisoning that shifts diagnostic thresholds, for example — that generic application security review does not catch. A dedicated clinical ML threat model is essential before launch.
Sequencing
Builds on Evaluation-Driven Clinical AI
Threat Modeling for ML across other industries
Structured analysis of prompt injection, data poisoning and model-specific attack surfaces.
Threat Modeling for ML/LLM Systems
Structured analysis of prompt injection, data poisoning and model-specific attack surfaces.
Threat Modeling for ML Trading Systems
Structured analysis of adversarial and manipulation risks specific to ML-driven trading.
Threat Modeling for OT / ICS Systems
Structured analysis of adversarial and safety risks specific to converged IT/OT environments.
Threat Modeling for Commerce & Payment Systems
Structured analysis of fraud, scraping and AI-agent-specific risks in commerce systems.
From the Healthcare & Life Sciences Emerging Technology & Innovation Radar, Edition 2026.1, published 2026-10-01.
An independent, industry-agnostic radar curated by Shailesh Kumar.