Book Image

Zero Trust Overview and Playbook Introduction

By : Mark Simos, Nikhil Kumar
5 (1)
Book Image

Zero Trust Overview and Playbook Introduction

5 (1)
By: Mark Simos, Nikhil Kumar

Overview of this book

Zero Trust is cybersecurity for the digital era and cloud computing, protecting business assets anywhere on any network. By going beyond traditional network perimeter approaches to security, Zero Trust helps you keep up with ever-evolving threats. The playbook series provides simple, clear, and actionable guidance that fully answers your questions on Zero Trust using current threats, real-world implementation experiences, and open global standards. The Zero Trust playbook series guides you with specific role-by-role actionable information for planning, executing, and operating Zero Trust from the boardroom to technical reality. This first book in the series helps you understand what Zero Trust is, why it’s important for you, and what success looks like. You’ll learn about the driving forces behind Zero Trust – security threats, digital and cloud transformations, business disruptions, business resilience, agility, and adaptability. The six-stage playbook process and real-world examples will guide you through cultural, technical, and other critical elements for success. By the end of this book, you’ll have understood how to start and run your Zero Trust journey with clarity and confidence using this one-of-a-kind series that answers the why, what, and how of Zero Trust!
Table of Contents (13 chapters)
Free Chapter
2
Chapter 2: Reading the Zero Trust Playbook Series

What is AI?

AI is the simulation of intelligent human behavior using computers. There are many focus areas within AI, so this book will only provide a brief summary of AI and its implications for security and Zero Trust.

It’s important to distinguish between two different types of AI capabilities:

  • Classic AI: The role of AI in security began by capturing and scaling expert human experience over large datasets with machine learning (ML). This takes the form of human experts training and tuning supervised ML models ahead of time and having unsupervised ML models identify clusters or patterns in the data that they surface to human experts for analysis. ML enables humans to identify patterns and anomalies in large amounts of data that can be used to identify security weaknesses, attacks, and other insights in the large complex technical estates of a modern organization.
  • Generative AI: Recently, large language models (LLM) have enabled the analysis and generation of...