You're reading from Hybrid Cloud Security Patterns Leverage modern repeatable architecture patterns to secure your workloads on the cloud

Product type Paperback

Published in Nov 2022

Publisher Packt

ISBN-13 9781803233581

Length 252 pages

Edition 1st Edition

Concepts

Cloud Computing

Author (1):

Sreekanth Iyer

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Table of Contents (18) Chapters

Preface

1. Part 1: Introduction to Cloud Security

2. Chapter 1: Opportunities and Challenges with Hybrid Multi-cloud Solutions FREE CHAPTER

3. Chapter 2: Understanding Shared Responsibility Model for Cloud Security

4. Part 2: Identity and Access Management Patterns

5. Chapter 3: Cloud Identity and Access Management

6. Chapter 4: Implementing Identity and Access Management for Cloud Applications

7. Part 3: Infrastructure Security Patterns

8. Chapter 5: How to Secure Compute Infrastructure

9. Chapter 6: Implementing Network Isolation, Secure Connectivity, and Protection

10. Part 4: Data and Application Security Patterns

11. Chapter 7: Data Security Patterns

12. Chapter 8: Shift Left Security for DevOps

13. Part 5: Cloud Security Posture Management and Zero Trust Architecture

14. Chapter 9: Managing the Security Posture for Your Cloud Deployments

15. Chapter 10: Building Zero Trust Architecture with Hybrid Cloud Security Patterns

16. Index

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Data classification and monitoring patterns

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Problem

How do we discover the data and classify its existence to determine the protection strategy and mechanisms? The problem is also about solving how to monitor the access of data and alert on any important event or breach.

Context

Before determining the protection of data, enterprises need to acknowledge the existence of data. Then, it is required to classify the data based on the sensitivity to determine what data protection and monitoring strategy needs to be followed.

Many enterprises are unaware of their data distribution and sensitive content residing outside of expected security protocol. The other problem is that these organizations retain far too much data that may be redundant, obsolete, or trivial. Redundant data is data that is duplicated, and obsolete data has passed its useful life. Data that has no ongoing business value is considered trivial. Typical enterprises struggle with dark...