The AI systems you're shipping have a design too. Model it.
Models, agents, and the pipelines that feed them introduce components and trust boundaries that traditional reviews never had to account for. ThreatModeler® Nexus™ brings the same architecture-first discipline to AI systems, so the risk in how they're designed is understood before it reaches production.
Finding flaws is fast now. Knowing what's missing is the hard part.
AI writes more of the code, and AI is more of the system.
Teams are securing software that AI helped write, and securing the AI systems themselves. Both need a current picture of the intended design, because a model can find what's present in code but not the control that should exist and doesn't.
The platform you use to secure AI is governed itself.
ThreatModeler Nexus is a threat modeling platform first: it shows what could go wrong in a system so you can design the risk out. When that system is an AI system, the same discipline applies, and the platform's own AI runs under enterprise control.
Model the AI system
Capture the models, data flows, and trust boundaries of an AI system as a threat model, so its design risk is visible the way any other system's is.
Facts, not assumptions
The Secure Design Graph anchors the model in the system's real decisions and context, turning an inferred picture of AI-generated code into ground truth you can reason about.
AI under your control
Bring-your-own-AI operates within a governed, deterministic framework, on your architectural context and approved security content, with no hard-coded keys.
The threats unique to AI systems, named and mapped.
AI systems introduce components and attack surfaces that traditional threat modeling was never designed to cover. The Secure Design Graph includes curated threat libraries specific to AI and ML systems, grounded in the frameworks that define how they fail.
Prompt injection and model manipulation
Prompt injection attacks, jailbreaks, and adversarial input attacks targeting model behavior. MITRE ATLAS categorizes these at the framework level; the Secure Design Graph maps them to your system's specific AI components and data flows.
Data poisoning and supply chain
Training data poisoning, backdoor attacks, and model supply chain compromise: threats that operate at the data and pipeline level rather than at inference time, and require a design-level view of the ML pipeline to find.
Model theft and extraction
Model inversion, model extraction, and membership inference attacks that target the model itself as an asset. Understanding these requires a threat model of how the model is served, queried, and protected, not just a scan of the code around it.
Methodologies built for how AI systems actually fail.
OWASP and MITRE have both extended their frameworks to cover AI-specific threats. ThreatModeler Nexus includes curated coverage for MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems), OWASP Agentic AI Threats & Mitigations (2025), and MAESTRO for enterprise agentic system alignment. These complement STRIDE and PASTA for AI components, rather than replacing them.