Threat Modeling for ML/LLM Systems
Structured analysis of prompt injection, data poisoning and model-specific attack surfaces.
Traditional application threat models miss entire classes of LLM-specific risk. Every team shipping agentic or tool-using AI should run a dedicated ML/LLM threat model before launch.
Signals behind this placement
- —Conventional application threat models do not cover prompt injection or tool-call abuse.
- —Agentic systems expand the attack surface faster than security review cadences adapt.
Sequencing
Builds on Agentic Workflows
Threat Modeling for ML across other industries
Structured analysis of prompt injection, data poisoning and model-specific attack surfaces.
Threat Modeling for Clinical ML Systems
Structured analysis of adversarial and failure risks specific to clinical AI.
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 Technology & SaaS Emerging Technology & Innovation Radar, Edition 2026.1, published 2026-10-01.
An independent, industry-agnostic radar curated by Shailesh Kumar.