Mastering Generative AI Security: Advanced Defenses for LLM & RAG Systems

What you will learn:

Analyze the expanding attack surface of Generative AI systems, including models, data pipelines, and external tools.
Implement a holistic AI security architecture to strategically map and apply protective measures across every subsystem.
Construct detailed threat scenarios for Large Language Model (LLM) applications and select appropriate defensive mechanisms.
Establish robust guardrail frameworks and policy enforcement engines to govern user interactions and model outputs effectively.
Embed critical security gates throughout the AI development and deployment lifecycle, from data validation to model assessments.
Configure secure authentication protocols, define precise permission boundaries, and manage tool capabilities for AI services.
Execute advanced data protection methodologies for RAG pipelines, incorporating content filtering, encryption, and granular access controls.
Utilize AI Security Posture Management (SPM) platforms to continuously inventory assets, identify misconfigurations, and monitor system behavioral drift.
Engineer comprehensive monitoring and observability pipelines to track user queries, model responses, tool invocations, and performance metrics.
Formulate a complete AI security control strategy and define actionable implementation plans for enterprise-wide adoption.

Description

The emergence of advanced AI systems, especially large language models (LLMs) and retrieval-augmented generation (RAG) pipelines, has fundamentally reshaped the cybersecurity landscape. Traditional defense mechanisms often prove inadequate against novel attack vectors that exploit prompts, tool integrations, and data flows within these intelligent applications. This course offers a comprehensive, hands-on framework designed to equip you with the knowledge and practical skills needed to secure contemporary GenAI deployments across real-world engineering environments.

You will gain deep insights into how modern AI threats operate, dissecting sophisticated attacks like prompt injections, data leakages via embeddings or model outputs, and unauthorized tool execution. We explore every critical layer of the AI application stack, demonstrating how to implement targeted, effective defenses through a structured and repeatable security methodology.

Key Learning Outcomes:

Architecting and understanding the complete AI Security Reference Framework, spanning model, prompt, data, tooling, and monitoring layers.
Deconstructing GenAI attack methodologies, including injection flaws, sensitive information exposure, misuse scenarios, and insecure tool execution.
Mastering the deployment of AI firewalls, intelligent filtering engines, and policy-driven controls for robust runtime protection.
Implementing AI-centric Secure Development Lifecycle (AI-SDLC) best practices, encompassing dataset validation, model evaluations, red teaming exercises, and version control.
Formulating advanced data governance strategies for RAG pipelines, covering access control lists (ACLs), encryption, content filtering, and secure embedding practices.
Designing identity and access management (IAM) patterns specifically tailored to safeguard AI endpoints and integrated toolchains.
Leveraging AI Security Posture Management (SPM) solutions for continuous risk scoring, drift detection, and automated policy enforcement.
Developing robust observability and evaluation pipelines to continuously monitor model behavior, performance, and reliability.

Included Resources & Practical Assets:

Detailed architecture blueprints and strategic control mapping guides.
Actionable threat modeling worksheets for LLMs and RAG systems.
Customizable governance templates and ready-to-implement security policies.
Essential checklists for AI-SDLC, RAG security, and data protection best practices.
Frameworks for comprehensive AI evaluation and firewall solution comparison.
A complete, actionable AI security control stack for immediate deployment.
A step-by-step 30, 60, 90-day rollout strategy for organizational adoption.

Why This Expertise is Critical:

Focuses on actionable security strategies for live AI deployments, moving beyond theoretical concepts.
Provides holistic coverage of every essential component within modern LLM and RAG architectures.
Delivers tangible, ready-to-use tools and artifacts, empowering immediate implementation.
Positions you at the forefront of one of the most rapidly expanding and high-demand domains in technology.

If you seek a structured, actionable blueprint for safeguarding AI systems against contemporary threats, this program delivers everything necessary to confidently secure, govern, and operate GenAI at enterprise scale.

Curriculum

Introduction

This introductory section lays the groundwork for an effective learning journey. It outlines the communication strategy for the course, provides essential tips to optimize your learning experience, and introduces the Learn IT Bot – a free AI-powered assistant designed to support your studies. Additionally, students gain access to an exclusive, free AI bot for hands-on practice without any sign-up requirements.

AI Cybersecurity Solutions

Dive deep into the core challenges and solutions for AI security. This section begins with a comprehensive mapping of the GenAI threat landscape, followed by an exploration of the foundational reference architecture for GenAI applications. It covers critical aspects of governance, policy, and compliance tailored for AI systems, along with practical methodologies for threat modeling. You'll learn about securing the AI Software Development Lifecycle (AI-SDLC), implementing robust AI firewalls and runtime protection mechanisms, and configuring API, identity, and access management specifically for AI environments. The module further delves into AI Security Posture Management (SPM), advanced data security and governance strategies for AI, common vulnerability classes with their mitigations, and essential observability and evaluation tools. Practical insights are provided through real-world AI security case studies, guidance on choosing between buying or building AI security solutions, and a comprehensive approach to designing an effective AI security control stack.

Threat Modeling for Agentic AI

This specialized section focuses on the emerging complexities of agentic AI. It starts by establishing the foundations of agentic AI systems, then dissects their unique threat landscape. You will learn specific techniques for threat modeling agentic systems, including detailed approaches for memory threat modeling and tooling threat modeling. The module emphasizes privilege and policy controls essential for securing autonomous agents and concludes with compelling case studies illustrating real-world failures and vulnerabilities in agentic AI.

Bonus section

This bonus lesson provides additional valuable insights and resources to further enhance your understanding and practical application of AI security principles.

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