AI Security: Red Teaming and Safety Filters

The rapid adoption of AI, particularly large language models, introduces new security challenges that demand proactive strategies. This course will equip you with the essential skills to understand, identify, and counter potential exploits, enabling you to build and deploy more secure AI applications. What you'll learn: * Understand core AI security concepts, including common vulnerabilities and attack vectors. * Learn the principles and methodologies of red teaming for AI systems. * Apply techniques to identify and exploit weaknesses in large language models through prompt manipulation. * Implement various safety filters and guardrails to protect AI models from malicious inputs and undesirable outputs. * Practice evaluating AI system robustness and resilience against adversarial attacks. * Configure basic observability and monitoring for detecting AI security incidents. * Develop strategies for responsible and ethical AI deployment with security in mind. This course begins with foundational concepts of AI security and common threats, then progresses to practical methodologies for red teaming and implementing defensive measures like safety filters. It is designed for beginners interested in AI security, AI developers, and anyone looking to build more resilient AI systems. No prior experience in AI security is required. Start building your expertise in AI security today.