Stanford Course - AI Security

Key Concepts:

• AI Security
• AI System Architectures
• Multi-Agent Setups
• Large Language Models (LLMs)
• Deep Fakes
• AI System Resilience
• Modern Threats

Introduction to AI Security

Neil Dwani, co-academic director of the Stanford online advanced cyber security program, introduces the new AI security course. The course focuses on the growing field of AI security and the potential risks involved in the design, build, and maintenance of AI systems. The primary goal is to provide participants with an understanding of how to make AI systems more secure and resilient against modern threats.

Course Content and Structure

The course is guided by world-renowned faculty and industry experts from leading companies. It aims to equip participants with the knowledge and skills necessary to safeguard their organizations. The curriculum covers the architectures of modern AI systems, including multi-agent setups.

Specific Threats and Vulnerabilities

The course will examine high-profile breaches involving large language models (LLMs) and deep fakes. It emphasizes that security considerations must extend beyond the specific issues of individual models and encompass the broader system architecture.

Building Trust and Resilience

The course acknowledges that while AI models will continue to improve, it is also crucial to develop mechanisms and strategies to build trust in these systems. The ultimate objective is to empower participants with the critical skills needed to fortify AI systems and enhance their resilience against evolving threats.

Conclusion

The Stanford online AI security course aims to provide a comprehensive understanding of AI security, covering system architectures, specific threats like LLM breaches and deep fakes, and strategies for building trust and resilience. The course is designed to equip participants with the skills needed to protect their organizations from modern AI-related threats.