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

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. Spis treści: 1. Assessing the Vulnerability of Your Algorithms, Models, and AI Environments2. Understanding the Most Common Machine Learning Attacks3. Planning for Regulatory Compliance4. Data Protection and Governance5. Data Privacy and Responsible AI Best Practices6. Managing and Securing Access7. Managing and Securing Your Azure Machine Learning Workspace8. Managing and Securing the MLOps Lifecycle9. Logging, Monitoring, and Threat Detection10. Setting a Security Baseline for Your Azure ML Workloads