The AI Red Teamer Job Role Path, in collaboration with Google, trains cybersecurity professionals to assess, exploit, and secure AI systems. Covering prompt injection, model privacy attacks, adversarial AI, supply chain risks, and deployment threats, it combines theory with hands-on exercises. Aligned with Google’s Secure AI Framework (SAIF), it ensures relevance to real-world AI security challenges. Learners will gain skills to manipulate model behaviors, develop AI-specific red teaming strategies, and perform offensive security testing against AI-driven applications. The path will be gradually expanded with related modules until its completion.
Hard
49 Sections
49 Sections
Required: 20
Reward: +20
2 Modules included
Medium
24 Sections
Reward: +10
This module provides a comprehensive guide to the theoretical foundations of Artificial Intelligence (AI). It covers various learning paradigms, including supervised, unsupervised, and reinforcement learning, providing a solid understanding of key algorithms and concepts.
24 Sections
Medium
25 Sections
Reward: +10
NEW
This module is a practical introduction to building AI models that can be applied to various infosec domains. It covers setting up a controlled AI environment using Miniconda for package management and JupyterLab for interactive experimentation. Students will learn to handle datasets, preprocess and transform data, and implement structured workflows for tasks such as spam classification, network anomaly detection, and malware classification. Throughout the module, learners will explore essential Python libraries like Scikit-learn and PyTorch, understand effective approaches to dataset processing, and become familiar with common evaluation metrics, enabling them to navigate the entire lifecycle of AI model development and experimentation.
25 Sections