Machine Learning Security with Azure: Best practices for assessing, securing, and monitoring Azure Machine Learning workloads

by Georgia Kalyva

Industry best practices to identify vulnerabilities and protect your data, models, environment and applications, or recover from a security breach when working with Azure machine learning. With AI and machine learning models gaining popularity and integrating into more and more applications, it is more important than ever to ensure that models perform accurately and are not vulnerable to cyber-attacks. However, attacks can target your data or environment as well. This book helps you identify security risks and apply the best practices to protect your assets on multiple levels, from data and models to your applications and infrastructure. We begin by introducing what common machine learning attacks are, how to identify risks, and the industry standards and responsible AI principles you need to follow to gain an understanding of what you need to protect. Next, you will learn the best practices to secure your workloads. Starting with data protection and governance and then moving on to protecting your infrastructure, you will gain insights on managing access and securing your Azure ML workspace. Then we move to DevOps practices to automate tasks securely and ways to recover from ML attacks. Finally, you will learn how to set a security benchmark for your scenario and best practices to maintain and monitor your security posture. By the end of this book, you will be able to implement the best practices to assess and secure your machine learning assets throughout the Azure Machine Learning lifecycle. This book is for anyone looking to learn how to assess, secure, and monitor every aspect of AI or Machine Learning projects running on the Microsoft Azure platform using the latest security and compliance industry best practices and standards. For machine learning developers and data scientists working on ML projects, learning this is a must. IT administrators, DevOps, and security engineers required to secure and monitor Azure workloads will also benefit from learning the best practices outlined in this book, as it covers everything from implementation to deployment and AI attack prevention and recovery.

310 pages, Paperback

Published December 28, 2023