Securing the AI Revolution: Defense-in-Depth for Large Language Models

What you'll learn
- The AI Threat Landscape: Understand the Rise of LLMs and New Attack Surfaces that differ fundamentally from traditional web application security.
- The Blueprint for Defense: Master the OWASP Top 10 for LLM Applications and learn to assess risks based on Prevalence, Exploitability, and Impact.
- Secure Foundations: Integrate LLM security into the Secure Development Lifecycle (SDLC) with a practical Whiteboard Solution for risk analysis and mitigation
- The #1 Risk: Deep dive into LLM01: Prompt Injection and distinguish between direct and indirect attacks.
- Strategic Layering: Implement a robust Defense-in-Depth architecture tailored for LLMs.
- Vector Database Security: Understand the emerging threats and best practices for LLM08: Vector and Embedding Weaknesses.
- Protecting Your Secrets: Implement controls against LLM02: Sensitive Information Disclosure and prevent model over-sharing.

Requirements
- No Pre Requirements

Description
"This course contains the use of artificial intelligence."

Course Overview

The integration ofLarge Language Models (LLMs)into enterprise applications has opened up a powerful new frontier—and a dangerous new attack surface.

This comprehensive course is designed to equip developers, security professionals, and architects with the specialized knowledge and practical skills needed to build, deploy, and manage secure LLM applications.

You won't just learnwhatthe risks are; you'll learnhow to proactively prevent them.

We will dive deep into theOWASP Top 10 for LLM Applications, providing clear, actionable strategies for mitigating the most critical vulnerabilities at every stage of the development lifecycle.

What You Will Learn

Module 1: Introduction to LLM Security and the OWASP Top 10

The AI Threat Landscape:Understand theRise of LLMs and New Attack Surfacesthat differ fundamentally from traditional web application security.

The Blueprint for Defense:Master theOWASP Top 10 for LLM Applicationsand learn to assess risks based onPrevalence, Exploitability, and Impact.

Secure Foundations:Integrate LLM security into theSecure Development Lifecycle (SDLC)with a practicalWhiteboard Solutionfor risk analysis and mitigation.

Module 2: The Critical Input and Output Vulnerabilities

The #1 Risk:Deep dive intoLLM01: Prompt Injectionand distinguish between direct and indirect attacks.

Defense Against Data Poisoning:AddressLLM07: System Prompt Leakageand secure the core instructions of your model.

Post-Processing Security:Learn best practices to prevent vulnerabilities like XSS throughLLM05: Improper Output Handling.

Module 3: Data, Model, and Supply Chain Risks

Protecting Your Secrets:Implement controls againstLLM02: Sensitive Information Disclosureand prevent model over-sharing.

Integrity is Key:Explore the threats ofLLM04: Data and Model Poisoningand how they compromise model reliability.

Third-Party Trust:Secure your reliance on external components by mitigatingLLM03: Supply Chain Vulnerabilitieswith a dedicatedWhiteboard Solution.

Module 4: Runtime, Control, and Operational Issues

Controlling the Model's Power:AddressLLM06: Excessive Agencyusing the Principle of Least Privilege and human-in-the-loop controls.

Beyond the Code:Explore operational risks likeLLM09: Misinformation and Overrelianceand guard against financial attacks throughLLM10: Unbounded Consumption.

Vector Database Security:Understand the emerging threats and best practices forLLM08: Vector and Embedding Weaknesses.

Module 5: Defense-in-Depth and Future Trends

Strategic Layering:Implement a robustDefense-in-Deptharchitecture tailored for LLMs.

Active Testing:MasterBest Practices for LLM Security Testing, includingRed TeamingandAdversarial Testing.

Staying Ahead:Establish protocols forMonitoring, Logging, and Incident Responseand prepare forThe Future of LLM Security and Emerging Threats.

Who Should Attend?

Software Developersbuilding LLM-enabled features.

Security Engineersresponsible for application and AI security.

DevOps/MLOps Engineersmanaging LLM deployment and infrastructure.

Technical Product Managersoverseeing AI product development.

Enroll now to transform from simply using LLMs to building genuinely secure AI applications.

Who this course is for:
- Software Developers building LLM-enabled features.
- Security Engineers responsible for application and AI security.
- DevOps/MLOps Engineers managing LLM deployment and infrastructure
- Technical Product Managers overseeing AI product development.
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