Secure ChatGPT, Claude, AI Agents & RAG Applications with Prompt Injection Defense, Guardrails, OWASP LLM Top 10
What You Will Learn:
- Identify and understand major LLM and Generative AI security threats, including prompt injection, jailbreaking, data leakage, insecure output handling
- Apply the OWASP guidance for LLM and Generative AI applications to assess vulnerabilities and strengthen AI application security.
- Design and secure Retrieval-Augmented Generation (RAG) systems against document poisoning, retrieval attacks, unauthorized access, and vector database risks.
- Protect AI agents and tool-calling systems using least privilege, permission controls, human approval workflows, and secure MCP practices.
- Secure AI APIs and integrations using authentication, authorization, secrets management, rate limiting, and secure API design principles.
- Implement AI guardrails using input filtering, output validation, content moderation, policy enforcement, and human-in-the-loop controls.
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Overview: Beyond the Hype of AI Security
Let’s be honest: the tech world is currently obsessed with building AI, but we are collectively failing at securing it. I’ve spent years in cybersecurity, and the shift from traditional application security to LLM & Generative AI Security feels like moving from a fenced backyard into a wild, unpredictable jungle. This course isn’t just another “how to use ChatGPT” tutorial; it’s a deep dive into the vulnerability research and defensive architecture required to keep modern enterprises from leaking data through their own chatbots.
What I appreciated most is that it doesn’t treat AI as a “black box.” Instead, it dissects the entire pipeline. We’re moving past the “jailbreaking for fun” phase and into the “how do I stop a malicious actor from poisoning my Vector Database” phase. The course focuses heavily on the shift from deterministic code to probabilistic models, which is a massive hurdle for traditional DevSecOps teams. It addresses the reality that you can’t just “patch” a LLM the way you patch a server; you have to build a multi-layered defense strategy involving AI guardrails and robust input filtering.
The curriculum stays grounded in reality. While many courses get bogged down in theoretical AI ethics, this one stays in the trenches of threat modeling and secure API design. It bridges the gap between the beginner to advanced levels by starting with the basics of prompt injection and scaling up to complex AI agentic workflows where the risks of unauthorized tool-calling can lead to actual infrastructure compromise. It’s an essential reality check for anyone thinking that a simple “system prompt” is enough to keep their data safe.
Prerequisites: What You Actually Need
To get the most out of this, you shouldn’t be a total stranger to code. While you don’t need to be a data scientist, a working knowledge of Python is pretty much mandatory since most of the hands-on labs rely on it. You should also have a baseline understanding of how APIs work—think RESTful principles and authentication (JWTs, API keys). If you’ve never heard of the OWASP Top 10, you might want to do a quick Google search first, as the course uses the OWASP LLM Top 10 framework as its primary compass for assessing vulnerabilities.
Skills & Tools: The Modern AI Security Stack
This is where the course earns its keep. You aren’t just reading slides; you’re working with industry-standard tools and frameworks. Key skills and tools covered include:
- OWASP LLM Top 10: Learning how to map real-world projects to established security standards.
- Guardrail Frameworks: Implementing NeMo Guardrails or similar tools for output validation and content moderation.
- RAG Security: Hardening Retrieval-Augmented Generation pipelines against document poisoning and retrieval-side attacks.
- Vector DB Protection: Securing instances of Pinecone, Weaviate, or Milvus against unauthorized access.
- Model Context Protocol (MCP): Understanding the security implications of AI agents and their ability to execute code or call external functions.
- Secrets Management: Ensuring that AI APIs and integrations don’t become the source of your next major data breach.
Career Benefits & Job Roles: Becoming “Job-Ready”
The demand for AI Security Engineers is skyrocketing, yet the talent pool is incredibly shallow. Completing this course serves as excellent certification prep for anyone looking to pivot into specialized security roles. By building job-ready skills, you position yourself as the person who can tell the C-suite “Yes, we can use Claude and GPT-4, and here is exactly how we do it without ending up in the news.”
Potential job roles that benefit from this expertise include:
- AI Security Architect: Designing the high-level permission controls and human-in-the-loop workflows for corporate AI platforms.
- AppSec Engineer: Modernizing traditional security audits to include jailbreaking tests and insecure output handling checks.
- DevSecOps Specialist: Integrating AI guardrails into the CI/CD pipeline for GenAI applications.
- Security Consultant: Offering vulnerability assessment services specifically for startups building AI agents and RAG systems.
Investing in this knowledge is a massive catalyst for career growth, especially as AI application security moves from a niche interest to a board-level priority.
Pros
- Extremely Practical: The focus on real-world projects and hands-on labs means you actually know how to implement these defenses, not just talk about them.
- Comprehensive Framework: Using the OWASP LLM Top 10 provides a structured, professional language for discussing security threats with stakeholders.
- Forward-Thinking: It covers AI agents and tool-calling, which is where the industry is heading (and where the most dangerous vulnerabilities currently hide).
Cons
- Fast-Paced Evolution: Because the field of Generative AI moves at breakneck speed, some of the specific libraries or API versions mentioned might change by the time you finish the course, requiring you to do a bit of independent troubleshooting to keep your hands-on labs running smoothly.