Model Armor: Securing AI Deployments
This course explains how to use Model Armor to protect AI applications, specifically large language models (LLMs). The curriculum covers Model Armor's architecture and its role in mitigating threats like malicious URLs, prompt injection, jailbreaking, sensitive data leaks, and improper output handling. Practical skills include defining floor settings, configuring templates, and enabling various detection types. You'll also explore sample audit logs to find details about flagged violations.
Ce que vous allez apprendre
- Explain the purpose of Model Armor in a company's security portfolio.
- Define the protections that Model Armor applies to all interactions with the LLM.
- Set up the Model Armor API and find flagged violations.
- Identify how Model Armor manages prompts and responses.
Prérequis
- Working knowledge of APIs
- Working knowledge of Google Cloud CLI
- Working knowledge of cloud security foundational principles
- Familiarity with the Google Cloud console
Public cible
- Security engineers, AI/ML developers, cloud architects
Programme de la Formation
6 modules pour maîtriser les fondamentaux
Objectifs
- Recall the course learning objectives.
Sujets abordés
- →What's in it for me?
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
- 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).
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.
Prochaines sessions
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- Tarifs dégressifs (plusieurs places)
- Session privée ou sur-mesure
- En présentiel ou à distance