Preface
Machine learning (ML) and artificial intelligence (AI) have continued to evolve rapidly in the past two years, with significant advancements and applications in various fields. AI and ML systems often process vast amounts of sensitive data, including personal information. Ensuring the security of this data is crucial to protect against breaches that could lead to identity theft, financial fraud, and other privacy violations. On top of this, governments and regulatory bodies are implementing stricter data protection and privacy laws. Compliance with these regulations is essential for legal and ethical operations. This is why securing those systems has become more vital than ever. As cyber threats evolve, AI and ML systems must be designed to adapt and respond to new and emerging security challenges, ensuring long-term resilience and reliability.
If you are working with Azure Machine Learning, this book will help you assess the vulnerability of data, models, and environments and implement the best practices to manage, secure, and monitor Azure Machine Learning workloads throughout the ML life cycle.
This book starts by providing an overview of what you need to protect. This includes learning about the Zero Trust strategy, using the MITRE ATLAS framework to understand ML attacks, and learning how to work ethically and responsibly, by using multiple services to help you stay compliant with industry standards and regulations. If you have never worked with Azure Machine Learning, you will also find a project in the beginning to get started. From there on, the book focuses on data and all the best practices to protect it. That includes everything from developing a data management framework to data encryption, backup, and recovery best practices. Following that, the book focuses on any infrastructure that surrounds Azure Machine Learning workloads, starting from identity and access and then going through networking and compute best practices. Finally, it provides all the needed information to automate these processes and monitor the system to prevent, detect, and mitigate any issues, and provides an overview of threat modeling to help you re-assess and keep your Azure Machine Learning workloads secure.
By the end of this book, you will be able to implement the best practices to assess and secure your Azure Machine Learning assets throughout the ML life cycle.
