Secure AI: Red-Teaming & Safety Filters

As large language models revolutionize business operations, sophisticated attackers exploit AI systems through prompt injection, jailbreaking, and content manipulation—vulnerabilities that traditional security tools cannot detect. This intensive course empowers AI developers, cybersecurity professionals, and IT managers to systematically identify and mitigate LLM-specific threats before deployment. Master red-teaming methodologies using industry-standard tools like PyRIT, NVIDIA Garak, and Promptfoo to uncover hidden vulnerabilities through adversarial testing. Learn to design and implement multi-layered content-safety filters that block sophisticated bypass attempts while maintaining system functionality. Through hands-on labs, you'll establish resilience baselines, implement continuous monitoring systems, and create adaptive defenses that strengthen over time. This course is designed for AI engineers, security professionals, data scientists, and developers interested in ensuring the safety and robustness of AI models. It’s also ideal for technology leaders seeking to implement secure, responsible AI frameworks within their organizations. Learners should have a basic understanding of machine learning, AI model architecture, and programming concepts. No prior experience with AI red-teaming or safety systems is required. By end of this course, you'll confidently conduct professional AI security assessments, deploy robust safety mechanisms, and protect LLM applications from evolving attack vectors in production environments

• Design red-teaming scenarios to identify vulnerabilities and attack vectors in large language models
• Implement content-safety filters to detect and mitigate harmful outputs while maintaining model performance
• Evaluate and enhance LLM resilience by analyzing adversarial inputs and developing defense strategies

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work