Secure AI · AI Threat Modeling
Design AI systems that fail safely
AI systems blur the line between data and instructions, and agents act with real permissions. We threat-model your AI architecture before it ships, covering model inputs, retrieval sources, tool permissions, memory and human oversight, so you build the right guardrails from day one.
Outcomes
What you walk away with
Clear map of where untrusted content can steer your AI
Least-privilege design for agent tools and credentials
Human-in-the-loop checkpoints where they actually matter
Evidence of responsible AI design for customers and regulators
Coverage
What's in scope
What we model
- Prompt and context assembly, including retrieved content
- Agent tools, MCP servers, plugins and their permissions
- Memory, multi-agent communication and delegation
- Training/fine-tuning data and model supply chain
- Output handling and downstream integrations
Frameworks
- STRIDE adapted for AI components
- MAESTRO for agentic systems
- OWASP LLM and agentic threat taxonomies
- MITRE ATLAS adversary techniques
Deliverables
- AI system data-flow diagram with trust boundaries
- AI-specific threat register with mitigations
- Guardrail and agent-permission design recommendations
- Test plan that feeds directly into AI security testing
Standards & frameworks
- MITRE ATLAS
- OWASP Top 10 for LLM Applications
- MAESTRO
- NIST AI RMF
- ISO/IEC 42001
Ideal for
- Teams designing agents that take real-world actions
- Companies adding AI to products that hold sensitive data
- Organisations preparing for AI governance requirements
FAQ
AI Threat Modeling questions
How is AI threat modeling different from regular threat modeling?
The model itself is an untrusted component: any content it reads can act as instructions. That changes where trust boundaries sit and what controls work, especially once agents can call tools.
Related
Often combined with
AI & LLM Security Testing
LLM and AI chatbot penetration testing for RAG pipelines, AI agents and MCP servers: prompt injection, data leakage, tool abuse.
AI Application Security
Secure code review and guardrail engineering for the code that wires LLMs into your product.
Threat Modeling
Map how your system can be attacked before code ships, and decide which controls actually matter.
Ready to talk about ai threat modeling?
Tell us what you're building. In a free 30-minute scoping call we'll map your risks and recommend the right engagement, whether you need a pentest, a threat model or an AI security review.