GCP200VERTEXAISEC

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.

✓ Formation officielle Google CloudNiveau Intermediate⏱️ 2 jours (14h)

Ce que vous allez apprendre

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.

Prérequis

Fundamental knowledge of machine learning, in particular generative AI, and basic understanding of security on Google Cloud.

Public cible

AI practitioners, security professionals, cloud architects

Programme de la Formation

8 modules pour maîtriser les fondamentaux

Objectifs

Review Google Cloud Security fundamentals.
Establish a foundational understanding of Vertex AI.
Enumerate the security concerns related to Vertex AI features and components.

Sujets abordés

→Google Cloud Security
→Vertex AI components
→Vertex AI Security concerns

Activités

Lab: Vertex AI: Training and Serving a Custom Model

Quality Process

SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach

Teaching Methods Used

Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
Guided Labs — Guided practical exercises on software, hardware, or technical environments.
Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).

Evaluation and Monitoring System

The achievement of training objectives is evaluated at multiple levels to ensure quality:

Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.