Book Image

The Cybersecurity Playbook for Modern Enterprises

By : Jeremy Wittkop
Book Image

The Cybersecurity Playbook for Modern Enterprises

By: Jeremy Wittkop

Overview of this book

Security is everyone's responsibility and for any organization, the focus should be to educate their employees about the different types of security attacks and how to ensure that security is not compromised. This cybersecurity book starts by defining the modern security and regulatory landscape, helping you understand the challenges related to human behavior and how attacks take place. You'll then see how to build effective cybersecurity awareness and modern information security programs. Once you've learned about the challenges in securing a modern enterprise, the book will take you through solutions or alternative approaches to overcome those issues and explain the importance of technologies such as cloud access security brokers, identity and access management solutions, and endpoint security platforms. As you advance, you'll discover how automation plays an important role in solving some key challenges and controlling long-term costs while building a maturing program. Toward the end, you'll also find tips and tricks to keep yourself and your loved ones safe from an increasingly dangerous digital world. By the end of this book, you'll have gained a holistic understanding of cybersecurity and how it evolves to meet the challenges of today and tomorrow.
Table of Contents (15 chapters)
Section 1 – Modern Security Challenges
Section 2 – Building an Effective Program
Section 3 – Solutions to Common Problems

Gathering data and applying context

A general rule concerning machine learning specifically, and automation techniques generally, is that they require large amounts of data to be effective. More data will enable the machine to make better decisions and solve more complex problems. Part of the data that can be gathered will help the machines apply context to what they are seeing. Currently, algorithms struggle with qualitative analysis. Algorithms that can tell you what happened using a large dataset are commodities at this point. This is not to say these algorithms are not helpful, they are simply common. Some algorithms are also predictive. With enough historical data, some algorithms have become good at predicting what will happen next. This is largely based on pattern recognition and determining the next logical data point given the historical data. People should be very careful with predictive algorithms because incomplete datasets can lead to poor predictions. Also, machines have...