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Machine Learning Security with Azure

You're reading from  Machine Learning Security with Azure

Product type Book
Published in Dec 2023
Publisher Packt
ISBN-13 9781805120483
Pages 310 pages
Edition 1st Edition
Languages
Author (1):
Georgia Kalyva Georgia Kalyva
Profile icon Georgia Kalyva

Table of Contents (17) Chapters

Preface 1. Part 1: Planning for Azure Machine Learning Security
2. Chapter 1: Assessing the Vulnerability of Your Algorithms, Models, and AI Environments 3. Chapter 2: Understanding the Most Common Machine Learning Attacks 4. Chapter 3: Planning for Regulatory Compliance 5. Part 2: Securing Your Data
6. Chapter 4: Data Protection and Governance 7. Chapter 5: Data Privacy and Responsible AI Best Practices 8. Part 3: Securing and Monitoring Your AI Environment
9. Chapter 6: Managing and Securing Access 10. Chapter 7: Managing and Securing Your Azure Machine Learning Workspace 11. Chapter 8: Managing and Securing the MLOps Life Cycle 12. Chapter 9: Logging, Monitoring, and Threat Detection 13. Part 4: Best Practices for Enterprise Security in Azure Machine Learning
14. Chapter 10: Setting a Security Baseline for Your Azure Machine Learning Workloads 15. Index 16. Other Books You May Enjoy

Summary

In this chapter, we learned to utilize multiple services, ensuring we can monitor our resources effectively by enabling different services and learning how we can start to use the logs we gather to prevent security incidents. The first one we worked with was Azure Monitor, using Monitor alerts to make sure we can be notified about any issues. By combining the capabilities of Monitor Log Analytics and Application Insights, we can have end-to-end monitoring of our resources and our model endpoints. Additionally, by using Microsoft Defender for Cloud, we can get recommendations to implement best practices, and we can use Microsoft Sentinel for advanced threat management. Now that we have a comprehensive view of the best practices across different surface areas included in a ML project, we can combine them and see how we can build a security baseline for our Azure resources in the next chapter.

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