Training Google Cloud

Training goals

code: GC-T-VAIGAS-I | version: 1.0

This course is designed to empower your organization to fully harness the transformative potential of Google’s Agent Platform 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 Agent Platform and its security challenges.
  • Implement identity and access control measures to restrict access to
  • Agent Platform resources.
  • Configure encryption strategies and protect sensitive information.
  • Enable logging, monitoring, and alerting for real-time security oversight of Agent Platform 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

AI practitioners, security professionals, and cloud architects.

 

Products

  • Agent Platform
  • Sensitive Data Protection
  • Gemini Enterprise
  • Agent Engine Threat Detection
  • Model Armor
  • Agent Studio on Gemini Enterprise Agent Platform
  • Access Context Manager
  • Privileged Access Manager
  • Advanced Networking
  • Confidential External Key Manager
  • Google Cloud Observability
  • Gemini
  • Cloud IAM
  • Cloud VPC
  • Cloud KMS
  • Cloud Operations

 

 

 

Conspect Show list

  • Module 1 - Introduction to Agent Platform Security Principles
    • Topics:
      • Google Cloud Security
      • Agent Platform components
      • Agent Platform Security concerns
    • Objectives:
      • Review Google Cloud Security fundamentals.
      • Establish a foundational understanding of Agent Platform.
      • Enumerate the security concerns related to Agent Platform features and components.
    • Activities:
      • Lab: Agent Platform: Training and Serving a Custom Model
  • Module 2 - Identity and Access Management (IAM) in Agent Platform
    • 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
  • Module 3 - 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
  • Module 4 - Securing Agent Platform 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
  • Module 5 - Monitoring and logging in Agent Platform
    • Topics:
      • Logging
      • Monitoring
    • Objectives:
      • Write to and analyze logs.
      • Set up monitoring and alerting.
  • Module 6 - 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 Agent Platform Gemini API
      • Lab: Gen AI & LLM Security for Developers
  • Module 7 - 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.
  • Module 8 - 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 Agent Platform 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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