Course Overview of Model Armor: Securing AI Deployments

Model Armor: Securing AI Deployments is an introductory-level course designed for security engineers, AI/ML developers, and cloud architects responsible for protecting AI applications and Large Language Models (LLMs). The course provides a comprehensive overview of Model Armor’s architecture, security capabilities, and deployment practices for securing AI interactions. 

Participants will learn how Model Armor mitigates common LLM security threats, configure security guardrails, customize protection templates, enable detection mechanisms, review audit logs, and integrate Model Armor APIs into AI applications. Through demonstrations, labs, and practical examples, learners gain the knowledge required to build secure and compliant AI solutions.  

After completing Model Armor: Securing AI Deployments, students will be able to:

  • Understand the purpose and architecture of Model Armor.
  • Identify common LLM security threats and vulnerabilities.
  • Map Model Armor features to AI security risks.
  • Configure floor settings and security guardrails.
  • Create and customize Model Armor templates.
  • Enable and configure detection mechanisms.
  • Set up and use the Model Armor API.
  • Analyze audit logs and flagged violations.
  • Secure AI prompts and responses using Model Armor controls.

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Key Features of Model Armor: Securing AI Deployments

  • AI Security Fundamentals

  • LLM Threat Protection

  • Prompt Injection Defense

  • Sensitive Data Protection

  • Custom Security Guardrails

  • Model Armor API Integration

  • Audit Logging and Violation Monitoring

  • Secure Prompt and Response Management

Who should attend Model Armor: Securing AI Deployments?

  • Security Engineers
  • AI/ML Developers
  • Cloud Architects
  • Security Architects
  • Platform Engineers
  • AI Security Professionals
  • Risk and Compliance Teams
  • Cloud Security Practitioners

Prerequisites of Model Armor: Securing AI Deployments

  • Working knowledge of APIs.  
  • Working knowledge of Google Cloud CLI.  
  • Understanding of cloud security foundational principles.  
  • Familiarity with the Google Cloud Console.

Why choose CloudThat as your training for partner for Model Armor: Securing AI Deployments?

Learning Objectives Model Armor: Securing AI Deployments

  • Understand the purpose and architecture of Model Armor.  
  • Identify common LLM security threats and vulnerabilities.  
  • Map Model Armor features to AI security risks.  
  • Configure floor settings and security guardrails.  
  • Create and customize Model Armor templates
  • Enable and configure detection mechanisms.  
  • Set up and use the Model Armor API.  
  • Analyze audit logs and flagged violations.  
  • Secure AI prompts and responses using Model Armor controls.  

Course Outline for Model Armor: Securing AI Deployments Download Course Outline

Topics

  • What's in it for Me?
  • Course Learning Objectives

Learning Outcomes

  • Understand the course structure and objectives.
  • Recognize the importance of AI security.
  • Identify key learning goals for Model Armor adoption.

Activities

  • Course Introduction
  • Learning Objectives Review

Topics

  • About Model Armor
  • LLM Security Risks
  • Model Armor Architecture
  • OWASP LLM Vulnerabilities

Learning Outcomes

  • Explain the purpose of Model Armor.
  • Identify AI security risks addressed by Model Armor.
  • Understand Model Armor architecture and components.
  • Map security controls to threat mitigation strategies.

Activities

  • Knowledge Check
  • Quiz

Topics

  • Model Armor Customization
  • Floor Settings
  • Guardrails and Confidence Levels
  • Templates
  • Detection Configuration

Learning Outcomes

  • Configure Model Armor protection settings.
  • Understand floor settings and their functionality.
  • Create and manage security templates.
  • Configure multiple detection types.
  • Apply security controls to AI applications.

Activities

  • Knowledge Check
  • Quiz

Topics

  • Model Armor API Setup
  • API Prerequisites
  • Flagged Violations
  • Audit Logging
  • Security Command Center Integration

Learning Outcomes

  • Enable and configure the Model Armor API.
  • Understand API integration requirements.
  • Monitor security violations and findings.
  • Analyze audit logs within Security Command Center.
  • Resolve floor setting violations.

Activities

  • Hands-On Lab
  • Quiz

Topics

  • Prompts and Responses
  • Application Code Integration
  • Prompt Sanitization
  • Response Filtering

Learning Outcomes

  • Understand prompt and response inspection workflows.
  • Secure AI interactions using Model Armor.
  • Apply prompt sanitization techniques.
  • Configure response filtering and protection policies.
  • Implement secure AI application patterns.

Activities

  • Security Configuration Demonstration
  • Prompt Protection Exercises
  • Quiz

Topics

  • What Did I Learn?
  • Course Summary

Learning Outcomes

  • Review key security concepts.
  • Summarize Model Armor capabilities.
  • Identify next steps for AI security implementation.

Activities

  • Course Recap
  • Final Discussion

Certification Details of Model Armor: Securing AI Deployments

    Participants receive a course completion certificate upon successfully completing the training program.

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Course ID: 29947

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FAQs for Model Armor: Securing AI Deployments

This course is designed for security engineers, AI/ML developers, cloud architects, and professionals responsible for securing AI applications.

Model Armor is a Google Cloud security solution that protects AI applications and LLMs from threats such as prompt injection, jailbreaking, malicious URLs, sensitive data leaks, and unsafe outputs.

Yes. The course includes hands-on labs, demonstrations, API setup exercises, and security configuration activities.

The course covers prompt injection, jailbreaking, malicious URLs, sensitive data exposure, improper output handling, and selected OWASP LLM vulnerabilities.

Yes. Participants learn how to configure floor settings, templates, guardrails, detections, logging, and API integrations.

Yes. Learners explore audit logs, flagged violations, Security Command Center integration, and violation management workflows.

The course is delivered in a 3-hour instructor-led format.

Organizations can strengthen AI security, reduce risk exposure, improve compliance, protect sensitive information, and establish secure AI deployment practices using Model Armor.

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