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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

Exploring threat management with Sentinel

Microsoft Sentinel is a cloud-native Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) solution. It offers intelligent security analytics and threat intelligence centrally for Azure and other clouds. With Sentinel, we have smart alert detection, threat visibility, hunting, and response, all in a single pane. There are several benefits to using Sentinel for the aforementioned tasks:

  • As a cloud solution, it scales with our data, and we pay for what we use.
  • Microsoft Sentinel gathers data using connectors from a wide range of sources, including Azure services, on-premises environments, and other clouds.
  • The service comes with built-in ML models that help to identify suspicious activities and reduce false positives. Over time, these models can be trained to improve their accuracy based on your organization’s unique patterns.
  • Threat hunting is done using KQL to...
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