Vertex AI and Generative AI Security
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.
- Duration: 2 days
- Languages: English, German
- Level: Intermediate
- Group size: Up to 16
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.
Prerequisites
- Fundamental knowledge of machine learning, in particular generative AI.
- Basic understanding of security on Google Cloud.
Course outline
Introduction to Vertex AI Security Principles
- Google Cloud Security
- Vertex AI components
- Vertex AI Security concerns
Identity and Access Management (IAM) in Vertex AI
- Overview of IAM in Google Cloud
Data Security and Privacy
- Data encryption
- Protecting Sensitive Data
- VPC Service Controls
- Disaster recovery planning
Securing Vertex AI Endpoints and model deployment
- Network security
- Securing model endpoints
- Deploy ML models using model endpoints
- Secure model endpoints
Monitoring and logging in Vertex AI
- Logging
- Monitoring
Security risks in generative AI applications
- Overview of gen AI security risks
- Overview of AI Safety
- Prompt security
- LLM safeguards
Testing and evaluating generative AI model responses
- Testing generative AI model responses
- Evaluating model responses
- Fine-Tuning LLMs
Securing Retrieval-Augmented Generation (RAG) systems
- Fundamentals of Retrieval-Augmented Generation
- Security in RAG systems
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Not sure which course fits?
Book a 30-minute call. We'll look at where your team is, what they need to be able to do, and put together either a specific course date or a tailored learning plan.