What You'll Learn
- Understand the full GenAI threat landscape and how modern attacks target LLMs and RAG systems
- Apply the AI Security Reference Architecture to design secure AI applications
- Perform threat modeling for GenAI systems and map risks to concrete mitigations
- Implement AI firewalls, filtering rules, and runtime protection controls
- Build a secure AI SDLC with dataset security, evals, and red-teaming practices
- Configure identity, access, and permission models for AI tools and endpoints
- 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
- Assemble an end-to-end AI security control stack and build a 30/60/90 day roadmap
Requirements
- Intro level understanding of how modern applications or cloud systems work
- Optional familiarity with machine learning or LLM based tools
- Some exposure to security fundamentals is useful but not mandatory
- Comfort with technical documentation and architectural schematics
- No background in AI security or specialized tooling required
Who This Course is For
- Software developers building or integrating AI features
- ML and AI engineers working with LLMs or RAG pipelines
- Architects designing secure AI driven systems
- Data engineers and data scientists handling AI datasets
- Security engineers and DevSecOps teams supporting AI workloads
- Technical leads and managers responsible for AI adoption and risk management
Your Instructor
Andrii Piatakha
Founder and CEO in IT-Bulls, Founder of Learn-IT University
4.4 Instructor Rating
50,365 Reviews
1,191,106 Students
82 Courses
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