AI systems introduce risks that traditional security cannot handle. LLM powered applications, retrieval pipelines, agents, vector databases, and tool integrations open new vulnerabilities that organizations struggle to understand and control. This course gives you a complete, practical, end to end framework for securing real GenAI workloads in production environments.

You will learn how modern AI attacks actually work, how to map threats across every layer of an LLM or RAG system, and how to implement controls that prevent data leakage, prompt manipulation, unsafe tool execution, and misconfigured connectors. The course is fully aligned with the way enterprises deploy and operate AI today, combining architecture, security engineering, data governance, and monitoring into one unified approach.

What this course covers

• A full breakdown of the AI Security Reference Architecture

• Real world GenAI threats: prompt injection, data exposure, model exploitation

• AI firewalls, guardrails, filtering engines, and safe tool permission models

• AI SDLC practices: provenance, evaluations, red teaming, versioning

• Data governance for RAG pipelines: ACLs, filtering, encryption, secure embeddings

• Identity and access patterns for AI endpoints and tool integrations

• AI Security Posture Management: asset inventory, risk scoring, drift detection

• Observability, telemetry, and evaluation workflows for production AI

A full breakdown of the AI Security Reference Architecture

Real world GenAI threats: prompt injection, data exposure, model exploitation

AI firewalls, guardrails, filtering engines, and safe tool permission models

AI SDLC practices: provenance, evaluations, red teaming, versioning

Data governance for RAG pipelines: ACLs, filtering, encryption, secure embeddings

Identity and access patterns for AI endpoints and tool integrations

AI Security Posture Management: asset inventory, risk scoring, drift detection

Observability, telemetry, and evaluation workflows for production AI

What you receive

• Architecture diagrams

• Threat modeling templates

• Security and governance policies

• AI SDLC and RAG security checklists

• Evaluation and firewall comparison matrices

• A complete AI security control stack

• Practical rollout plan for the first 30, 60, and 90 days

Architecture diagrams

Threat modeling templates

Security and governance policies

AI SDLC and RAG security checklists

Evaluation and firewall comparison matrices

A complete AI security control stack

Practical rollout plan for the first 30, 60, and 90 days

Why this course matters

• It is practical, not theoretical

• It focuses on real AI attack surfaces, not generic cybersecurity

• It gives you the frameworks, controls, and artifacts needed to secure enterprise AI

• It prepares you for the growing demand for engineers who understand AI security at depth

It is practical, not theoretical

It focuses on real AI attack surfaces, not generic cybersecurity

It gives you the frameworks, controls, and artifacts needed to secure enterprise AI

It prepares you for the growing demand for engineers who understand AI security at depth

If you need a focused, well structured, and actionable guide to securing modern AI systems, this course gives you everything required to build, defend, and operate safe and reliable GenAI applications from day one.