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Course Outline
Introduction to AI Threat Modeling
- What makes AI systems vulnerable?
- AI attack surface vs traditional systems
- Key attack vectors: data, model, output, and interface layers
Adversarial Attacks on AI Models
- Understanding adversarial examples and perturbation techniques
- White-box vs black-box attacks
- FGSM, PGD, and DeepFool methods
- Visualizing and crafting adversarial samples
Model Inversion and Privacy Leakage
- Inferring training data from model output
- Membership inference attacks
- Privacy risks in classification and generative models
Data Poisoning and Backdoor Injections
- How poisoned data influences model behavior
- Trigger-based backdoors and Trojan attacks
- Detection and sanitization strategies
Robustness and Defense Techniques
- Adversarial training and data augmentation
- Gradient masking and input preprocessing
- Model smoothing and regularization techniques
Privacy-Preserving AI Defenses
- Introduction to differential privacy
- Noise injection and privacy budgets
- Federated learning and secure aggregation
AI Security in Practice
- Threat-aware model evaluation and deployment
- Using ART (Adversarial Robustness Toolbox) in applied settings
- Industry case studies: real-world breaches and mitigations
Summary and Next Steps
Requirements
- An understanding of machine learning workflows and model training
- Experience with Python and common ML frameworks such as PyTorch or TensorFlow
- Familiarity with basic security or threat modeling concepts is helpful
Audience
- Machine learning engineers
- Cybersecurity analysts
- AI researchers and model validation teams
14 Hours
Testimonials (1)
The profesional knolage and the way how he presented it before us
