Security and Privacy in AI
Overview
Security and Privacy in AI is a comprehensive two-day course designed to equip participants with essential knowledge and skills to navigate the complex landscape of artificial intelligence security and privacy. This course addresses the growing need for professionals who can understand and mitigate the risks associated with AI systems while maximizing their potential benefits.
On the first day, participants delve into the fundamentals of AI security and privacy, exploring the value proposition of AI alongside its inherent risks. The course introduces key government and industry guidelines, including the NIST Artificial Intelligence Risk Management Framework, which provides a structured approach to identifying and managing AI-related risks. Participants also examine privacy considerations specific to AI systems and gain insights into the ISO/IEC CD 27090 guidance, which outlines security threats and potential failures in AI implementations. The second day focuses on practical aspects, covering attacks and defenses for Large Language Models (LLMs), AI observability techniques, secure coding practices tailored for AI development, and access control mechanisms for AI systems. By the end of the course, attendees will have developed a comprehensive understanding of AI security and privacy issues, enabling them to reason about potential risks and implement effective mitigation strategies in their organizations.
Duration
1-2 days
Schedule
Register 21 days before class start date and save 10%! Enter discount code EARLY10 during registration.
Dates
Times
Location
Price
Why You Should Take This Course
In the duration of this course, students will:
- Understand the value and risks that AI can bring to an organization
- List the primary government and industry guidance directed at security and privacy in AI
- Reason about the risks involved with AI and how to mitigate those risks
- Learn the types of attacks that can be made against AI models and mitigation techniques
Course Outline
Security and Privacy in AI
Day 1
1. AI Security and Privacy Overview
2. NIST Artificial Intelligence Risk Management Framework
3. Privacy in AI Systems
4. ISO/IEC CD 27090 Guidance for security threats and failures in AI
Day 2
5. LLM Vulnerabilities and Mitigations
6. Observability for AI
7. Secure Coding Practices for AI
8. Access Control for AI