LLM Security: How to Protect Your Generative AI Investments

In this intermediate-level course, AI architect Adrián González Sánchez dives into the world of AI security and shows you how to secure large language models (LLMs) effectively. Learn about essential security techniques, from safeguarding infrastructure and networks to implementing access controls and monitoring systems. Discover strategies to protect against data leaks, adversarial attacks, and system vulnerabilities while leveraging AI technologies like ChatGPT, cloud-based APIs, and advanced generative models. Understand the practical applications of prompt engineering, retrieval augmented generation (RAG), and fine-tuning AI models for specific tasks. Explore real-world challenges and solutions and gain valuable insights into AI red teaming, regulatory compliance, and shared responsibility models. By the end of this course, you will be able to assess risk, implement security measures, and ensure your AI systems are both effective and secure.

Learning objectives
Analyze the current context of Generative AI adoption and identify the main security and safety challenges in the field.
Evaluate the various types of LLM attacks and their potential impact on AI systems.
Design a multi-level, onion-kind of approach to secure LLM and Generative AI implementations.
Apply LLMOps principles, countermeasures, and best practices to enhance the security of AI systems.
Create end-to-end architectures for LLM security and safety, with a focus on cloud technologies such as Microsoft Azure.