Training Google Cloud

Training goals

code: G-VAIGAIS | version: 1.0

This course is designed to empower your organization to fully harness the transformative potential of Google’s Vertex AI and generative AI (gen AI) technologies, with a strong emphasis on security. Tailored for AI practitioners and security engineers, it provides targeted knowledge and hands-on skills to navigate and adopt AI safely and effectively. Participants will gain practical insights and develop a security-conscious approach, ensuring a secure and responsible integration of gen AI within their organization.

 

What you'll learn

  • Establish foundational knowledge of Vertex AI and its security challenges.
  • Implement identity and access control measures to restrict access to Vertex AI resources.
  • Configure encryption strategies and protect sensitive information.
  • Enable logging, monitoring, and alerting for real-time security oversight of Vertex AI operations.
  • Identify and mitigate unique security threats associated with generative AI.
  • Apply testing techniques to validate and secure generative AI model responses.
  • Implement best practices for securing data sources and responses within Retrieval-Augmented Generation (RAG) systems.
  • Establish foundational knowledge of AI Safety.

 

Audience

This course is primarily intended for AI practitioners, security professionals, and cloud architects.

 

Products

  • Vertex AI
  • Gemini
  • Cloud IAM
  • Cloud VPC
  • Cloud KMS
  • Cloud Operations
  • Sensitive Data Protection

Conspect Show list

  • Introduction to Vertex AI Security Principles
    • Topics
      • Google Cloud Security
      • Vertex AI components
      • Vertex AI Security concerns
    • Objectives
      • Review Google Cloud Security fundamentals.
      • Establish a foundational understanding of Vertex AI.
      • Enumerate the security concerns related to Vertex AI features and components.
    • Activities
      • Lab: Vertex AI: Training and Serving a Custom Model
  • Identity and Access Management (IAM) in Vertex AI
    • Topics
      • Overview of IAM in Google Cloud
    • Objectives
      • Control access with Identity Access Management.
      • Simplify permission using organization hierarchies and policies.
      • Use service accounts for least privileged access.
    • Activities
      • Lab: Service Accounts and Roles: Fundamentals
  • Data Security and Privacy
    • Topics
      • Data encryption
      • Protecting Sensitive Data
      • VPC Service Controls
      • Disaster recovery planning
    • Objectives
      • Configure encryption at rest and in-transit.
      • Encrypt data using customer-managed encryption keys.
      • Protect sensitive data using the Data Loss Prevention service.
      • Prevent exfiltration of data using VPC Service Controls.
      • Architect systems with disaster recovery in mind.
    • Activities
      • Lab: Getting Started with Cloud KMS
      • Lab: Creating a De-identified Copy of Data in Cloud Storage
  • Securing Vertex AI Endpoints and model deployment
    • Topics
      • Network security
      • Securing model endpoints
    • Objectives
      • Deploy ML models using model endpoints.
      • Secure model endpoints.
    • Activities
      • Lab: Configuring Private Google Access and Cloud NAT
  • Monitoring and logging in Vertex AI
    • Topics
      • Logging
      • Monitoring
    • Objectives
      • Write to and analyze logs.
      • Set up monitoring and alerting.
  • Security risks in generative AI applications
    • Topics
      • Overview of gen AI security risks
      • Overview of AI Safety
      • Prompt security
      • LLM safeguards
    • Objectives
      • Identify security risks specific to LLMs and gen AI applications.
      • Understand methods for mitigating prompt hacking and injection attacks.
      • Explore the fundamentals of securing generative AI models and applications.
      • Introduce fundamentals of AI Safety.
    • Activities
      • Lab: Safeguarding with Vertex AI Gemini API
      • Lab: Gen AI & LLM Security for Developers
  • Testing and evaluating generative AI model responses
    • Topics
      • Testing generative AI model responses.
      • Evaluating model responses.
      • Fine-Tuning LLMs.
    • Objectives
      • Implement best practices for testing model responses.
      • Apply techniques for improving response security in gen AI applications.
    • Activities
      • Lab: Measure Gen AI Performance with the Generative AI Evaluation Service
      • Lab: Unit Testing Generative AI Applications
  • Securing Retrieval-Augmented Generation (RAG) systems
    • Topics
      • Fundamentals of Retrieval-Augmented Generation
      • Security in RAG systems
    • Objectives
      • Understand RAG architecture and security implications.
      • Implement best practices for grounding and securing data sources in RAG systems.
    • Activities
      • Lab: Multimodal Retrieval Augmented Generation (RAG) Using the Vertex AI Gemini API
      • Lab: Introduction to Function Calling with Gemini
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Additional information

Prerequisites

Fundamental knowledge of machine learning, in particular generative AI, and basic understanding of security on Google Cloud.
Difficulty level
Duration 2 days
Certificate

The participants will obtain certificates signed by Google Cloud (course completion).
Trainer

Authorized Google Cloud Trainer

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