Learn to identify, analyze, and mitigate GenAI threats using modern security playbooks

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

Description
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 acomplete, practical, architecture driven guideto securing real GenAI systems end to end. No fluff, no theory for theory's sake. Only actionable engineering practices, proven controls, and real world templates.

What this course delivers

A full AI security blueprint, including:

AI Security Reference Architecture for model, prompt, data, tools, and monitoring layers

The complete GenAI threat landscape and how attacks actually work

AI firewalls, runtime guardrails, policy engines, and safe tool execution

AI SDLC workflows: dataset security, red teaming, evals, versioning

RAG data governance: ACLs, filtering, encryption, secure embeddings

Access control and identity for AI endpoints and tool integrations

AI SPM: asset inventory, drift detection, policy violations, risk scoring

Observability and evaluation pipelines for behavior, quality, and safety

What you gain

You getpractical, ready to use artifacts, including:

Reference architectures

Threat modeling worksheets

Security and governance templates

RAG and AI SDLC checklists

Firewall evaluation matrix

End to end security control stack

A 30, 60, 90 day implementation roadmap

Why this course stands out

Focused entirely onreal engineering and real security controls

Covers thefull AI stack, not just prompts or firewalls

Gives you tools used by enterprises adopting GenAI today

Helps you build expertise that israre, in demand, and highly valued

If you want a structured, practical, and complete guide to securing LLMs and RAG systems, this course gives you everything you need to design defenses, implement controls, and operate AI safely in production. This is the roadmap professionals use when they need to secure real AI systems the right way.

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
More Info