AI security is no longer optional. Modern LLMs, RAG pipelines, agents, vector databases, and AI powered tools introduce entirely new attack surfaces that traditional cybersecurity does not cover. Organizations face prompt injection, data leakage, model exploitation, unsafe tool calls, drift, misconfiguration, and unreliable governance. This course gives you a complete, practical, architecture driven guide to securing real GenAI systems end to end, including an AI Security Reference Architecture, threat modeling worksheets, AI firewalls, runtime guardrails, and a 30/60/90 day implementation roadmap.

This Course Offers

• The complete GenAI threat landscape and AI Security Reference Architecture: Understand how modern attacks target LLMs and RAG systems. Apply the AI Security Reference Architecture to design secure AI applications across model, prompt, data, tools, and monitoring layers.
• Threat modeling for GenAI systems and AI firewalls: Perform threat modeling for GenAI systems and map risks to concrete mitigations. Implement AI firewalls, filtering rules, runtime protection controls, policy engines, and safe tool execution.
• Secure AI SDLC with dataset security, evals, and red teaming: Build a secure AI SDLC including dataset security, evaluations, versioning, and red teaming practices. Configure identity, access, and permission models for AI tools and endpoints.
• RAG data governance and observability pipelines: Apply data governance techniques for RAG pipelines, embeddings, and connectors. Use SPM platforms to monitor drift, violations, and AI asset inventory. Deploy observability and evaluation tooling to track model behavior and quality.

Why We Love This Course

1. It is focused entirely on real engineering and real security controls. No fluff. No theory for theory's sake. Only actionable engineering practices, proven controls, and real world templates. One student review noted the course was very informative and in depth, with another calling it beautifully presented and easy to comprehend.
2. It covers the full AI stack, not just prompts or firewalls. You learn dataset security, RAG governance, access control, identity management, SPM platforms, observability pipelines, and evaluation tooling. This complete view helps you avoid security gaps that come from focusing only on one layer.
3. It gives you ready to use artifacts including reference architectures, threat modeling worksheets, security and governance templates, RAG and AI SDLC checklists, a firewall evaluation matrix, an end to end security control stack, and a 30/60/90 day implementation roadmap. You leave with materials you can use immediately.
4. It includes a free AI bot built by the instructor. Students get exclusive, free, no sign up access to an AI bot designed to help you learn the material, reinforce your knowledge, and gain a real advantage in interviews, real world work, and career growth.

Traditional cybersecurity does not cover AI attack surfaces. The question is whether you want to learn the practical controls, reference architectures, and implementation roadmaps for securing LLMs and RAG systems in production or leave your organization vulnerable to prompt injection, data leakage, and model exploitation.

• Software developers building or integrating AI features who need to understand security controls.
• ML and AI engineers working with LLMs or RAG pipelines who need to protect their systems.
• Architects designing secure AI driven systems who need reference architectures and threat models.
• Data engineers and data scientists handling AI datasets who need dataset security and governance.
• Security engineers and DevSecOps teams supporting AI workloads who need practical controls.
• Technical leads and managers responsible for AI adoption and risk management who need implementation roadmaps.

• An intro level understanding of how modern applications or cloud systems work is helpful.
• Optional familiarity with machine learning or LLM based tools is useful but not required.
• Some exposure to security fundamentals is useful but not mandatory.
• Comfort with technical documentation and architectural schematics is recommended.
• No background in AI security or specialized tooling is required.