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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 how to develop AI systems responsibly and how to develop an ethical approach using Responsible AI tools. We became familiar with the industry security standards and learned how to enforce them using the Azure Policy service. Reporting and automation for regulatory compliance were never easier as there are a lot of tools we can use to help us view and maintain the compliance status of our services. For reporting and auditing, we have the Compliance and Remediation blades in Azure Policy, Azure Resource Graph Explorer, and command-line tools. To automate environment creation, we can leverage the Azure Blueprints service and IaC.

Now that we have a strategy and some knowledge of multiple security standards available out of the box, let us see how we can implement all those controls and guardrails in our Azure environment. As always when it comes to ML, we will start with the data.

In the next chapter, we will explain data governance and how to...

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