What You'll Learn
- Identify modern GenAI risks and understand how attackers target LLM and RAG pipelines
- Apply a layered AI security design to strengthen every component of an AI application
- Create detailed AI threat models and link each threat to concrete control measures
- Configure AI firewalls and runtime guardrails to manage prompts, responses, and tool actions
- Embed security practices into AI development workflows, including dataset checks and eval automation
- Implement robust identity, authorization, and scoped access for AI endpoints and integrations
- Enforce data governance for RAG systems through access rules, tagging, and secure retrieval patterns
- Use SPM platforms to maintain visibility over models, datasets, connectors, and policy violations
- Build observability pipelines to track prompts, responses, decisions, and model quality metrics
- Assemble a unified AI security strategy and translate it into clear 30, 60, and 90 day actions
Requirements
- Some background in tech, engineering, or system development
- Optional exposure to machine learning concepts or LLM based tools
- Basic understanding of common security practices is a plus
- Ability to interpret high level architecture and process diagrams
- No previous experience with specialized AI security solutions required
Who This Course is For
- Developers integrating AI capabilities into existing or new products
- Machine learning engineers maintaining model workflows and RAG systems
- System and cloud architects designing secure AI infrastructures
- Security analysts and DevSecOps teams responsible for safeguarding AI services
- Team leads and decision makers who oversee AI initiatives and compliance requirements
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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