Secure AI apps, RAG, tools, memory, and agents while mastering risk, compliance, guardrails, and governance.
What You Will Learn:
- Identify major AI security threats across chatbots, RAG systems, tools, memory, and autonomous agents.
- Build and secure AI applications using Python, Ollama, RAG, tool calling, memory, and agents.
- Perform and defend against prompt injection, jailbreaks, document poisoning, and memory poisoning.
- Apply the OWASP Top 10 for LLM Applications to real-world AI systems.
- Implement prompt validation, risk scoring, guardrails, content filtering, and policy enforcement.
- Secure AI tool use with input validation, least-privilege permissions, and human approval workflows.
- Build an integrated AI Security Gateway for prompts, RAG, tools, memory, and agent actions.
- Create AI governance dashboards for inventory, usage, cost, risk, evaluation, drift, and compliance.
- Map AI controls to NIST AI RMF, ISO/IEC 42001, and the EU AI Act.
- Build an Enterprise AI Governance Command Center with audit trails, incidents, controls, evidence, and executive metrics.
Why Every Security Pro Needs to Stop Ignoring AI Security
Let’s be honest: most of us in the tech world spent the last eighteen months just trying to figure out how to get a Large Language Model (LLM) to stop hallucinating or to actually format JSON correctly. But while the developers were racing to push “AI-powered” features to production, a massive security debt was being built up in the background. I’ve sat through my fair share of “Intro to AI” courses that were 90% fluff and 10% marketing, but the AI Security & Governance Masterclass: Build, Attack & Defend is a different beast entirely. It’s the first time I’ve seen a course successfully bridge the gap between the “move fast” AI developer crowd and the “lock everything down” security team.
What makes this masterclass stand out is its build-attack-defend methodology. It’s one thing to read a whitepaper about prompt injection; it’s another thing entirely to write the exploit code yourself, watch the RAG system leak sensitive data, and then build the AI Security Gateway to prevent it. This isn’t just theory—it’s about gaining job-ready skills in a niche that is currently seeing a massive talent shortage.
Prerequisites for Success
You don’t need to be a data scientist with a PhD in linear algebra to get value out of this, but you shouldn’t go in totally green either. To really get the most out of the hands-on labs, you’ll want:
- Intermediate Python Skills: You need to be comfortable with APIs, environment variables, and basic asynchronous programming.
- Foundational Security Knowledge: If you understand the basics of the OWASP Top 10 (the traditional version), you’ll find the transition to the OWASP Top 10 for LLM Applications much smoother.
- Local Environment Setup: Be ready to run Ollama or similar local inference tools. The course moves away from just hitting OpenAI APIs to show you how to secure local, private AI deployments.
Mastering Industry-Standard Tools & Skills
The curriculum is a beginner to advanced journey that feels remarkably cohesive. You start by building standard Retrieval-Augmented Generation (RAG) systems, but quickly pivot into the “dark side.” You’ll dive deep into document poisoning—where you learn how an adversary can hide malicious instructions in a PDF that a RAG system might ingest—and memory poisoning, which is a massive concern for long-term autonomous agents.
On the governance side, the course moves into the boardroom. It covers the NIST AI Risk Management Framework (RMF) and the EU AI Act with a level of pragmatism I haven’t seen elsewhere. Instead of just listing the regulations, you actually build an Enterprise AI Governance Command Center. This means you’re learning to track model drift, audit trails, and cost management alongside your security controls.
Career Benefits & Emerging Job Roles
If you’re looking for career growth, this is the frontier. We are seeing a new class of job titles emerging—roles like AI Security Architect, AI Compliance Officer, and LLM Red Teamer. This course serves as excellent certification prep for those looking to pivot into these high-paying specializations. By completing real-world projects like building a custom Security Gateway, you end up with a portfolio that proves you can do more than just talk about AI—you can actually protect it. For CISOs and Security Engineers, this is about future-proofing your resume before the market gets saturated.
The Pros: Why This Course Hits the Mark
- The Integrated Approach: Most courses either do “The Hacking Part” or “The Compliance Part.” This course forces you to do both. You can’t truly govern what you don’t understand how to break.
- Local-First Learning: Using Ollama and local Python environments means you aren’t racking up a $200 API bill just to learn. It also reflects how enterprises are increasingly moving toward private, on-prem LLMs for data privacy.
- Practical Guardrails: The sections on least-privilege permissions for AI tools and human-in-the-loop workflows are pure gold for anyone actually deploying agents in a corporate environment.
- Actionable Governance: The AI Governance dashboards aren’t just pretty pictures; they are built on industry-standard tools and frameworks that you can take back to your boss on Monday morning.
The Cons: An Honest Critique
If there’s one “gotcha,” it’s the sheer density of the governance section. When you pivot from the adrenaline of performing a jailbreak to the granular details of ISO/IEC 42001 compliance, the pace can feel like it’s hitting a brick wall. It’s necessary information, but it requires a lot of mental switching between “hacker mode” and “auditor mode.” If you’re only interested in the technical exploits, you might find the EU AI Act modules a bit of a slog, but skipping them would be a mistake for your long-term career growth.