AI Systems Engineering and Cybersecurity: Design, Threat Modeling, and Defense Training by Tonex
This course offers a comprehensive exploration into the intersection of artificial intelligence, systems engineering, and cybersecurity. Participants will learn the principles of designing and engineering robust AI systems while understanding the cybersecurity threats unique to these technologies. The course covers best practices for threat modeling, risk assessment, and the development of effective mitigation strategies to ensure AI system integrity and reliability.
Learning Objectives:
By the end of this course, participants will be able to:
- Understand the core concepts of AI systems engineering and the cybersecurity landscape associated with AI technologies.
- Identify and analyze the vulnerabilities inherent in AI systems, including data, model, and algorithmic biases.
- Employ threat modeling techniques specific to AI and machine learning systems.
- Develop comprehensive mitigation strategies to defend AI systems against cyber threats.
- Integrate cybersecurity considerations throughout the AI system development lifecycle.
Target Audience:
- AI and machine learning engineers
- Cybersecurity professionals
- Systems engineers
- IT professionals interested in AI technologies
- Managers overseeing AI and cybersecurity operations
Course Outline:
Day 1: Foundations of AI Systems Engineering and Cybersecurity
Session 1: Introduction to AI Systems Engineering
- Overview of AI and machine learning concepts
- The role of systems engineering in AI
- Integrating AI into larger system architectures
Session 2: Cybersecurity Landscape for AI Systems
- Understanding the cybersecurity risks unique to AI
- Case studies of AI system breaches
- Regulatory and ethical considerations
Session 3: Vulnerabilities in AI Systems
- Data security and privacy concerns
- Model robustness and adversarial attacks
- Algorithmic transparency and accountability
Session 4: Threat Modeling for AI Systems
- Introduction to threat modeling methodologies
- Practicum: Applying STRIDE to AI systems
- Interactive Workshop: Building attack trees for AI systems
Day 2: Advanced Mitigation Strategies and Practical Applications
Session 5: Defensive Design in AI Systems
- Security-by-design principles for AI
- Encryption and secure data pipelines
- Authentication and access control mechanisms
Session 6: Risk Assessment and Management in AI
- Risk identification and impact analysis
- Quantitative and qualitative risk assessment techniques
- Risk management frameworks
Session 7: Implementing Mitigation Strategies
- AI-specific cybersecurity technologies
- Patch management and incident response planning
- Ongoing monitoring and maintenance protocols
Session 8: Cybersecurity in the AI System Development Lifecycle
- Secure development practices for AI
- DevSecOps in AI system engineering
- Lifecycle management and continuous improvement
Delivery Methods:
- Lectures and guest speaker sessions
- Case study analyses
- Group discussions
- Hands-on workshops and labs
- Interactive simulations
Assessment and Certification:
Participants will be assessed through a combination of quizzes, a final exam, and a capstone project involving the design of a secure AI system prototype.
Upon successful completion, participants will receive a certificate in “AI Systems Engineering and Cybersecurity.”
Materials Provided:
- Course notes and reference materials
- Access to AI system simulation environments
- Tools for threat modeling and risk assessment
This course is designed to be delivered over a period of five days but can be adjusted to fit different formats such as a week-long intensive course or spread over several weeks with online and in-person components.