Get in Touch

Course Outline

Introduction to AI Threat Modeling

  • What makes AI systems vulnerable?
  • Comparing the AI attack surface to traditional systems.
  • Key attack vectors: data, model, output, and interface layers.

Adversarial Attacks on AI Models

  • Understanding adversarial examples and perturbation techniques.
  • Differentiating between white-box and black-box attacks.
  • Exploring FGSM, PGD, and DeepFool methods.
  • Visualizing and crafting adversarial samples.

Model Inversion and Privacy Leakage

  • Inferring training data from model output.
  • Understanding membership inference attacks.
  • Analyzing privacy risks in classification and generative models.

Data Poisoning and Backdoor Injections

  • How poisoned data influences model behavior.
  • Trigger-based backdoors and Trojan attacks.
  • Strategies for detection and sanitization.

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

  • A solid understanding of machine learning workflows and model training processes.
  • Practical experience with Python and common ML frameworks such as PyTorch or TensorFlow.
  • Familiarity with basic security or threat modeling concepts is beneficial.

Audience

  • Machine learning engineers.
  • Cybersecurity analysts.
  • AI researchers and model validation teams.
 14 Hours

Number of participants


Price per participant

Testimonials (2)

Upcoming Courses

Related Categories