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

Implement industry best practices to identify vulnerabilities and protect your data, models, environment, and applications while learning how to recover from a security breach
Key Features

• Learn about machine learning attacks and assess your workloads for vulnerabilities
• Gain insights into securing data, infrastructure, and workloads effectively
• Discover how to set and maintain a better security posture with the Azure Machine Learning platform
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description
With AI and machine learning (ML) 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 cyberattacks. However, attacks can target your data or environment as well. This book will help you identify security risks and apply the best practices to protect your assets on multiple levels, from data and models to applications and infrastructure.
This book begins by introducing what some common ML attacks are, how to identify your 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 about the best practices to secure your assets. Starting with data protection and governance and then moving on to protect your infrastructure, you will gain insights into managing and securing your Azure ML workspace. This book introduces DevOps practices to automate your tasks securely and explains how 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’ll be able to implement best practices to assess and secure your ML assets throughout the Azure Machine Learning life cycle.
What you will learn

• Explore the Azure Machine Learning project life cycle and services
• Assess the vulnerability of your ML assets using the Zero Trust model
• Explore essential controls to ensure data governance and compliance in Azure
• Understand different methods to secure your data, models, and infrastructure against attacks
• Find out how to detect and remediate past or ongoing attacks
• Explore methods to recover from a security breach
• Monitor and maintain your security posture with the right tools and best practices

Who this book is for
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. This is a must-have resource for machine learning developers and data scientists working on ML projects. IT administrators, DevOps, and security engineers required to secure and monitor Azure workloads will also benefit from this book, as the chapters cover everything from implementation to deployment, AI attack prevention, and recovery.
Table of Contents

• Assessing the Vulnerability of Your Algorithms, Models, and AI Environments
• Understanding the Most Common Machine Learning Attacks
• Planning for Regulatory Compliance
• Data Protection and Governance
• Data Privacy and Responsible AI Best Practices
• Managing and Securing Access
• Managing and Securing Your Azure Machine Learning Workspace
• Managing and Securing the MLOps Lifecycle
• Logging, Monitoring, and Threat Detection
• Setting a Security Baseline for Your Azure ML Workloads