Threat Modeling for ML Trading Systems
Structured analysis of adversarial and manipulation risks specific to ML-driven trading.
ML-driven trading and risk systems face distinct adversarial risks — from data poisoning to market-manipulation-style gaming of model behaviour — that conventional security review does not adequately cover.
Sequencing
Builds on Explainable AI for Regulatory Compliance
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 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 Financial Services Emerging Technology & Innovation Radar, Edition 2026.1, published 2026-10-01.
An independent, industry-agnostic radar curated by Shailesh Kumar.