Book Image

Diving into Secure Access Service Edge

By : Jeremiah
Book Image

Diving into Secure Access Service Edge

By: Jeremiah

Overview of this book

The SASE concept was coined by Gartner after seeing a pattern emerge in cloud and SD-WAN projects where full security integration was needed. The market behavior lately has sparked something like a "space race" for all technology manufacturers and cloud service providers to offer a "SASE" solution. The current training available in the market is minimal and manufacturer-oriented, with new services being released every few weeks. Professional architects and engineers trying to implement SASE need to take a manufacturer-neutral approach. This guide provides a foundation for understanding SASE, but it also has a lasting impact because it not only addresses the problems that existed at the time of publication, but also provides a continual learning approach to successfully lead in a market that evolves every few weeks. Technology teams need a tool that provides a model to keep up with new information as it becomes available and stay ahead of market hype. With this book, you’ll learn about crucial models for SASE success in designing, building, deploying, and supporting operations to ensure the most positive user experience (UX). In addition to SASE, you’ll gain insight into SD-WAN design, DevOps, zero trust, and next-generation technical education methods.
Table of Contents (28 chapters)
1
Part 1 – SASE Market Perspective
7
Part 2 – SASE Technical Perspective
15
Part 3 – SASE Success Perspective
20
Part 4 – SASE Bonus Perspective
Appendix: SASE Terms

SASE Automate

Automation is simply the completion of work activities without dependency on human labor. Simple automation, such as a photocopy machine leverages, are triggered by human input. With the copy process, the activities performed by humans are placing the paper in the copy tray, selecting any desired settings, selecting the desired quantity, pushing the copy button, and retrieving the original along with the copies.

Complex automation still requires human input for success; however, tool systems such as AIOps can self-trigger activities to achieve specific outcomes based on policies designed by a human. With Machine Learning (ML), an AI-based platform can collect information from all available sources and run a training model to learn how to perform a specific task with desired outcomes. For instance, an organization wanting to automate service ticket resolution can train the AIOps solution to learn how to be a Microsoft Certified Systems Engineer (MCSE) as well as a Cisco...