AI systems introduce security challenges that are fundamentally different from anything traditional cybersecurity was built to handle. LLM applications, retrieval pipelines, vector databases, and agent based automations create new vulnerabilities that can expose sensitive data, enable unauthorized actions, and compromise entire workflows. This course gives you a complete and practical framework for securing GenAI systems in real engineering environments.

You will learn how modern AI threats operate, how attackers exploit prompts, tools, and connectors, and how data can leak through embeddings, retrieval layers, or model outputs. The course walks you through every layer of the AI stack and shows you how to apply the right defenses at the right places, using a structured and repeatable security approach.

What you will learn

• The full AI Security Reference Architecture across model, prompt, data, tools, and monitoring layers

• How GenAI attacks work, including injection, leakage, misuse, and unsafe tool execution

• How to use AI firewalls, filtering engines, and policy controls for runtime protection

• AI SDLC best practices for dataset security, evaluations, red teaming, and version management

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

• Identity and access patterns that protect AI endpoints and tool integrations

• AI Security Posture Management for risk scoring, drift detection, and policy enforcement

• Observability and evaluation workflows that track model behavior and reliability

The full AI Security Reference Architecture across model, prompt, data, tools, and monitoring layers

How GenAI attacks work, including injection, leakage, misuse, and unsafe tool execution

How to use AI firewalls, filtering engines, and policy controls for runtime protection

AI SDLC best practices for dataset security, evaluations, red teaming, and version management

Data governance strategies for RAG pipelines, ACLs, encryption, filtering, and secure embeddings

Identity and access patterns that protect AI endpoints and tool integrations

AI Security Posture Management for risk scoring, drift detection, and policy enforcement

Observability and evaluation workflows that track model behavior and reliability

What is included

• Architecture diagrams and control maps

• Model and RAG threat modeling worksheets

• Governance templates and security policies

• Checklists for AI SDLC, RAG security, and data protection

• Evaluation and firewall comparison frameworks

• A complete AI security control stack

• A step by step 30, 60, 90 day rollout plan for teams

Architecture diagrams and control maps

Model and RAG threat modeling worksheets

Governance templates and security policies

Checklists for AI SDLC, RAG security, and data protection

Evaluation and firewall comparison frameworks

A complete AI security control stack

A step by step 30, 60, 90 day rollout plan for teams

Why this course is essential

• It focuses on practical security for real AI deployments

• It covers every critical layer of modern LLM and RAG systems

• It delivers ready to use tools and artifacts, not theory

• It prepares you for one of the fastest growing and most demanded areas in tech

It focuses on practical security for real AI deployments

It covers every critical layer of modern LLM and RAG systems

It delivers ready to use tools and artifacts, not theory

It prepares you for one of the fastest growing and most demanded areas in tech

If you need a structured and actionable guide to protecting AI systems from modern threats, this course provides everything required to secure, govern, and operate GenAI at scale with confidence.