AI Security Fundamentals: Secure LLMs and AI Applications

This course contains the use of artificial intelligence.

AI is transforming software development, but every intelligent application introduces new security challenges. From chatbots and large language models to autonomous AI agents, organizations need professionals who understand how AI systems can be attacked—and how to defend them.

Welcome to AI Security Fundamentals: Secure LLMs and AI Applications, a practical course covering AI cybersecurity, Generative AI security, LLM security, and the controls used to protect real-world AI applications. You'll move beyond theory to explore common vulnerabilities, implement defenses, and test your work through guided, hands-on labs.

What You'll Learn

You'll start with AI security fundamentals, AI threat modeling, attack surfaces, and trust boundaries. Then you'll investigate the risks highlighted by the OWASP Top 10 for LLM Applications, including prompt injection, indirect prompt injection, sensitive data disclosure, data leakage, model supply chain attacks, and data poisoning.

You'll also explore Retrieval-Augmented Generation (RAG) security, vector database security, access control, insecure output handling, and system prompt leakage. As AI applications become more autonomous, you'll learn to reduce AI agent security risks, excessive agency, tool-calling vulnerabilities, and unauthorized actions through least-privilege access, authorization checks, and human approval workflows.

Security isn't only about finding vulnerabilities. You'll build defenses using input validation, output validation, PII redaction, authentication, API security, rate limiting, model integrity verification, and defense-in-depth architecture. You'll practice secure AI development, DevSecOps for AI, security testing, AI security monitoring, and incident response.

Learn by Building

Throughout the course, you'll work with BrightDesk, a fictional enterprise AI customer-support assistant. Step by step, you'll assess its architecture, identify threats, protect sensitive data, secure retrieval workflows, validate model artifacts, and control AI agent permissions.

Hands-on exercises use accessible tools such as Python, FastAPI, Ollama, FAISS, and pytest. You'll learn how to perform controlled tests in a local environment, evaluate defenses, and document residual risks. No paid AI API subscription is required, and starter examples make the practical work approachable.

You'll also become familiar with widely used guidance including NIST AI Risk Management Framework (AI RMF), MITRE ATLAS, and OWASP LLM security resources.

The final capstone connects everything: you will create a security checklist, map AI application vulnerabilities, add practical safeguards, run security regression tests, and present findings to stakeholders. This mirrors how real teams collaborate across engineering, architecture, privacy, and security—not by promising perfect protection, but by continually improving measurable controls over time.

Who Should Enroll?

This course is designed for software developers, AI engineers, solution architects, cybersecurity analysts, security engineers, DevSecOps professionals, and technical leaders responsible for deploying AI safely. Beginners are welcome; basic Python familiarity is helpful for coding exercises but not mandatory.

By the end, you'll have more than a vocabulary of AI threats. You'll have practiced applying security controls, producing an AI security risk assessment, and building a portfolio-ready secure AI application prototype. Whether you're securing your first LLM-powered chatbot or reviewing an enterprise AI architecture, you'll leave with a practical foundation for making AI applications more resilient.

Enroll and start building AI systems with security in mind from day one.