Red Teaming for Generative AI: Building Robust and Responsible Solutions

Course details

Almost 75% of organizations building generative AI solutions recognize the need for red teaming. This topic is so important to building generative AI solutions that White House and DEFCON recently hosted a Gen AI red teaming competition. Red teaming is a powerful cyber security technique that uncovers weaknesses and vulnerabilities in systems and organizations. In this course, technology leader Rashim Mogha illustrates how tech professionals can plan and implement red teaming to enhance security, reliability, and ethical behavior in generative AI solutions. Go over what red teaming is and how it enhances the security of your generative AI models. Learn how to find key vulnerabilities and risks that may accompany AI models. Explore a variety of red teaming techniques. Plus, learn how to mitigate the risks that red teaming helps you to identify.

Learning objectives
Define red teaming and its role in securing generative AI (Gen AI) models.
Identify key vulnerabilities and risks associated with Gen AI models.
Understand different red teaming techniques for testing Gen AI models.
Develop strategies to mitigate risks identified through red teaming.

Concepts

0. Introduction

01 - Leverage red teaming to build generative AI solutions

1. Introduction to Red Teaming for Generative AI

02 - Why red teaming for GenAI
03 - Understanding common attack vectors in generative AI systems
04 - Recognizing vulnerability implications

2. Red Teaming Techniques for GenAI

05 - Explore red teaming options for generative AI
06 - Tools and resources for red teaming

3. Red Teaming Process

07 - Building a red team
08 - Planning the test
09 - Conducting the test
10 - Reporting on the test

4. Mitigating Risks and Building Responsible AI

11 - Leveraging best practices for enhancing robustness
12 - Including security measures into the development lifecycle
13 - Leveraging continuous learning
14 - Understanding ethical implications of red teaming

Conclusion

15 - Next steps