This instructor-led course provides a focused understanding of securing Generative AI applications using Model Armor. Participants will explore key security risks in LLM-based systems and learn how to configure and enforce safety mechanisms such as guardrails, templates, and policy controls. 

Through practical examples and guided exercises, learners will gain hands-on experience in implementing Model Armor configurations, integrating APIs, and building secure AI-driven applications. 

After completing Securing Generative AI Applications with Model Armor, students will be able to:

  • Understand security risks associated with LLMs and Generative AI.
  • Explain the purpose and architecture of Model Armor.
  • Configure Model Armor settings including guardrails and templates.
  • Implement safety controls for prompts and responses.
  • Integrate Model Armor using APIs.
  • Detect and handle flagged violations.
  • Build secure GenAI applications with end-to-end protection.

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Key Features of Securing Generative AI Applications with Model Armor

  • 5 Focused Modules covering Model Armor lifecycle. 

  • Hands-on configuration of guardrails and templates. 

  • Real-world LLM security risk scenarios.

  • API-based integration with applications. 

  • Practical approach to securing prompts and responses.

Who should Attend Securing Generative AI Applications with Model Armor Course?

  • AI Developers
  • Generative AI Engineers
  • Application Developers
  • Security Engineers
  • Cloud Engineers
  • Solution Architects
  • AI Governance Professionals
  • DevSecOps Professionals
  • Professionals building secure AI-powered applications

Prerequisites of Securing Generative AI Applications with Model Armor

  • Basic understanding of Generative AI concepts
  • Familiarity with APIs and application development
  • Basic knowledge of cloud platforms (preferred)

    • Learning Objective of Securing Generative AI Applications with Model Armor

    • To enable learners to secure Generative AI applications using Model Armor by implementing guardrails, configuring policies, and mitigating risks in LLM-based systems. 

    Why choose CloudThat as your training partner?

    • Specialized Google Cloud AI and Security Expertise- CloudThat specializes in cloud, AI, and security technologies, delivering industry-focused training programs with practical implementation experience and enterprise use cases. 

    • Industry-Recognized Trainers- Our trainers are certified cloud and AI professionals with expertise in Generative AI, AI governance, AI security, and enterprise application development. 

    • Hands-On Learning Approach- CloudThat emphasizes practical learning through guided labs, implementation exercises, security scenarios, and real-world AI application workflows.

    • Customized Learning Paths- Training paths are designed for developers, AI engineers, architects, and security professionals with varying levels of expertise and AI adoption goals. 

    • Interactive and Practical Sessions- Sessions include demonstrations, architecture discussions, troubleshooting exercises, policy configuration, and collaborative learning activities.

    • Career and Certification Support- CloudThat supports learners with practical project guidance, interview preparation, and AI-focused cloud career learning paths.

    • Updated Industry-Relevant Content- Course content is continuously updated to align with the latest advancements in AI security, responsible AI, LLM governance, and enterprise AI technologies.

    • Trusted by Enterprises Worldwide- Thousands of professionals and organizations trust CloudThat for advanced cloud, AI, security, and Generative AI training programs.

    Course Outline for Securing Generative AI Applications with Model Armor Download Course Outline

    Lecture Content

    • Course Introduction
    • Learning Objectives
    • Agenda and Flow

    Lecture Content

    • Introduction to Model Armor
    • Key Features and Capabilities
    • LLM Security Risks (Prompt Injection, Data Leakage, Unsafe Output)

    Lecture Content

    • Customization Overview
    • Floor Settings Configuration
    • Guardrails and Confidence Levels
    • Template Configuration

    Lab Content

    • Configuring Guardrails and Templates

    Lecture Content

    • Enablement Options
    • API Setup and Integration
    • Handling Flagged Violations

    Lab Content

    • Integrating Model Armor APIs

    Lecture Content

    • Securing Prompts and Responses
    • Application Code Integration
    • End-to-End Workflow

    Lab Content

    • Building a Secure GenAI Application

    Lecture Content

    • Key Takeaways
    • Best Practices
    • Real-world Implementation Guidance

    Lab Content

    • Final Discussion / Wrap-up

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    Course ID: 28326

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    FAQ’s for Securing Generative AI Applications with Model Armor

    Developers, AI practitioners, and security engineers working with Generative AI applications.

    Model Armor, LLM security risks, guardrails, templates, and API integration.

    Basic knowledge of APIs and coding is helpful.

    1 day instructor-led training.

    Yes, practical labs for configuration and integration.

    Yes, including prompt injection and unsafe outputs.

    Yes, including real-world usage scenarios.

    Enquire Now