AI risk & threat modeling
Make risk visible before it ships.
Translate an AI architecture into a clear view of assets, abuse cases, and controls. Build a security plan around how your system actually works.
How does AI threat modeling help a product team?
AI threat modeling maps a system's assets, data flows, trust boundaries, and potential abuse cases before or during development. It helps teams choose proportionate controls, assign ownership, and decide what needs adversarial testing. Frameworks such as OWASP guidance, MITRE ATLAS, and NIST AI RMF can inform the work where relevant.
What we examine
- Data sources, model providers, integrations, and boundaries between trusted and untrusted content.
- Abuse cases involving data exposure, unauthorized actions, and unreliable outputs.
- Control ownership, residual risks, and decisions that require human review.
- Evaluation strategy, change management, and evidence needed for internal review.
When this is the right move
Organizations planning a new AI feature, reviewing a vendor, introducing agentic workflows, or formalizing ownership of AI risk.
Give engineering and business teams a shared basis for security decisions, with a practical plan for reducing the risks that matter most.