IT

Prerequisites

Participants should have:

• A basic understanding of AI principles (e.g. neural networks, training processes, common architectures)
• Familiarity with general security fundamentals
• No prior threat modelling experience is required
• Pre-course materials are provided to ensure readiness
• This course only runs as a private event, for cohorts of 8 people or more, ideally from the same organisation

Target audience

This course is intended for:

• AI engineers and machine learning developers
• Software engineers building AI-powered systems
• Solution architects integrating AI into enterprise applications
• Security professionals securing AI pipelines and APIs
• Security architects responsible for risk and compliance in AI deployments

AI Threat Modelling Certificate Course Content

Day 1: Foundations and methodology

Morning session

• Welcome and course orientation
• Introduction to AI-specific security risks and compliance requirements
• Threat modelling fundamentals in AI system development
• Comparison of traditional vs. AI threat modelling techniques
• Hands-on: Future-focused threat mapping exercise
• AI-DICE framework introduction
• AI system decomposition and trust boundary identification
• Data flow diagramming for AI workflows
• Hands-on: Diagramming an AI assistant's infrastructure

Afternoon session

• STRIDE-AI threat taxonomy and attack vectors
• Prompt injection, poisoning, model theft, and misuse scenarios
• Hands-on: Threat analysis using STRIDE-AI for UrbanFlow
• Attack tree development for AI use cases
• Using Mermaid to document and visualise attack paths
• Hands-on: Threat tree analysis of an autonomous vehicle system

Day 2: Implementation and defence

Morning session

• AI attack scenario analysis: prompt injection, data extraction, model manipulation
• Hands-on: The Curious Chatbot Challenge (LLM-based attack simulation)
• Overview of AI security libraries and frameworks:
  • OWASP Top 10 for LLMs
  • MITRE ATLAS
  • OWASP AI Exchange
  • MIT AI Risk Library
    • Hands-on: Applying OWASP AI Exchange to RAG-enabled CareBot

Afternoon session

• Security by design for AI systems:
  • Model lifecycle security
  • Data pipeline protection
  • API and endpoint hardening
    • Hands-on: AI Security Architecture design workshop
• AI risk assessment methodologies:
  • OWASP risk rating
  • Technical vs. business risk alignment
  • Risk matrices for AI
    • Hands-on: Healthcare robot risk assessment simulation

Day 3: Advanced concepts and practical application

Morning session

• AI governance and regulatory alignment:
  • Overview of GDPR, EU AI Act, and other standards
  • Ethical frameworks and explainability
  • Bias mitigation and transparency
    • Hands-on: The FairCredit AI ethics incident
• Privacy and safety in AI design:
  • Privacy-by-design in training and inference
  • Safe agent deployment and secure data handling
    • Hands-on: Data minimisation and AI privacy assessment

Afternoon session

• Securing MLOps and AI DevSecOps pipelines:
  • Threat modelling integration across lifecycle
  • Security incident handling for AI systems
    • Hands-on: Attack/control mapping in an MLOps architecture
• Final red/blue team wargame:
  • Simulated attack on a rogue AI research assistant
  • Threat identification, countermeasure design, and defence
  • Group debrief and lessons learned
• Course wrap-up and certification guidance

Exams and certification

Participants who complete the following will be awarded the AI Threat Modelling Practitioner Certificate:

• Completion of all hands-on exercises
• Creation and submission of an original AI threat model
• Passing the final examination, which is taken post class

Hands-on learning

This course includes:

• Live, instructor-led sessions and expert mentoring
• Scenario-based labs targeting real-world AI security use cases
• Red/blue team simulation for applied learning
• Ongoing access to pre-course study materials