Gen AI Security Foundations

Generative AI is transforming industries, but it also introduces new security risks that many organizations underestimate until a real incident occurs. This course, Gen AI Security Foundations, provides a practical and structured introduction to the most pressing security challenges that arise when working with Large Language Models (LLMs) and generative AI systems.

Across a series of focused lectures, participants will gain a comprehensive understanding of the OWASP Top 10 LLM Vulnerabilities for 2025, including threats such as prompt injection, model poisoning, sensitive data disclosure, improper output handling, excessive agency, vector database weaknesses, hallucination-induced misinformation, and unbound consumption attacks. Each vulnerability is explored through its technical background, real-world case studies, potential impacts, and proven mitigation strategies.

The training also maps vulnerabilities to the LLM development lifecycle—Training, Prompting, and Deployment—illustrating how risks emerge at different stages. Most importantly, the course emphasizes mitigation strategies. You will learn how to apply security best practices such as dataset validation, input-output sanitization, access controls, monitoring, and human-in-the-loop safeguards to reduce vulnerabilities in your AI systems.

By the end, you will be able to recognize, classify, and mitigate key LLM security risks while applying proven defense techniques to strengthen your AI solutions.

Whether you are a developer, architect, or security professional, this course equips you with the awareness and skills to harden AI systems and ensure safer, more trustworthy deployments in production environments.