AI Security Fundamentals: Risks, Frameworks & Tools

Modern AI applications introduce security challenges that traditional defenses cannot address. LLM based systems, retrieval pipelines, agents, data connectors, and vector databases expose new attack paths that organizations must understand and control. This course gives you a complete, practical, and engineering focused approach to securing GenAI systems across their entire lifecycle.
You will learn how attackers exploit AI models, how sensitive data leaks through prompts and outputs, how RAG pipelines can be manipulated, and how misconfigured tools or connectors expose entire environments. The course shows you how to design secure AI architectures, apply the right controls at the right layers, and build a repeatable security process for any AI powered system.

What this course includes
A detailed AI Security Reference Architecture for models, prompts, data, tools, and monitoring

Full coverage of GenAI threats: injection attacks, data leakage, model misuse, unsafe tools

Practical guardrail design using AI firewalls, filtering, and permissioning

AI SDLC guidance for dataset integrity, evaluations, red teaming, and version control

Data governance for RAG systems: access control, filtering logic, encryption, secure embeddings