Threat Modeling for Commerce & Payment Systems
Structured analysis of fraud, scraping and AI-agent-specific risks in commerce systems.
Agentic shopping traffic introduces new attack surface — from AI-agent account takeover to automated promo abuse — that conventional e-commerce fraud models were not built to detect.
Sequencing
Builds on Non-Human Identity for Commerce APIs
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 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.
From the Retail & Consumer Emerging Technology & Innovation Radar, Edition 2026.1, published 2026-10-01.
An independent, industry-agnostic radar curated by Shailesh Kumar.