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Course Outline

Introduction to AI Red Teaming

  • Understanding the AI threat landscape.
  • The roles of red teams in AI security.
  • Ethical and legal considerations.

Adversarial Machine Learning

  • Types of attacks: evasion, poisoning, extraction, inference.
  • Generating adversarial examples (e.g., FGSM, PGD).
  • Targeted vs. untargeted attacks and success metrics.

Testing Model Robustness

  • Evaluating robustness under perturbations.
  • Exploring model blind spots and failure modes.
  • Stress testing classification, vision, and NLP models.

Red Teaming AI Pipelines

  • Attack surface of AI pipelines: data, model, deployment.
  • Exploiting insecure model APIs and endpoints.
  • Reverse engineering model behavior and outputs.

Simulation and Tooling

  • Using the Adversarial Robustness Toolbox (ART).
  • Red teaming with tools like TextAttack and IBM ART.
  • Sandboxing, monitoring, and observability tools.

AI Red Team Strategy and Defense Collaboration

  • Developing red team exercises and goals.
  • Communicating findings to blue teams.
  • Integrating red teaming into AI risk management.

Summary and Next Steps

Requirements

  • A solid understanding of machine learning and deep learning architectures.
  • Practical experience with Python and machine learning frameworks (e.g., TensorFlow, PyTorch).
  • Familiarity with cybersecurity principles or offensive security methodologies.

Target Audience

  • Security researchers.
  • Offensive security teams.
  • AI assurance and red team professionals.
 14 Hours

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  • Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
  • Flexible Schedule: Dates and times adapted to your team's agenda.
  • Format: Online (live), In-company (at your offices), or Hybrid.
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Price per private group, online live training, starting from 2600 € + VAT*

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