What You'll Learn
- Examine how GenAI systems expand the attack surface across models, data, and tools
- Use an end to end AI security architecture to map protections onto each subsystem
- Develop comprehensive threat scenarios for LLM based applications and choose fitting safeguards
- Deploy guardrail frameworks and policy engines to control user inputs and model outputs
- Integrate security gates into AI delivery processes, covering data validation and model assessments
- Set up authentication flows, permission boundaries, and controlled tool capabilities for AI services
- Apply data protection practices to RAG pipelines, including filtering, encryption, and structured access
- Operate AI SPM solutions to track assets, detect misconfigurations, and monitor system drift
- Build monitoring pipelines that capture queries, responses, tool usage, and evaluation metrics
- Design a full AI security control map and plan actionable rollout steps for organizational adoption
Requirements
- No prior knowledge required
- A computer with internet access
- Eagerness to learn
Who This Course is For
- Professionals building or maintaining applications enhanced with generative AI
- ML specialists working with embeddings, retrievers, and model endpoints
- Architects responsible for structuring secure AI and data pipelines
- Security teams evaluating risks in AI powered systems
- Leaders and practitioners managing AI adoption, governance, and operational safety
Your Instructor
Experienced Instructor
Professional Educator
An experienced instructor with expertise in this field and a passion for teaching.
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