Data-Driven Cybersecurity: Reducing risk with proven metrics

Measure, improve, and communicate the value of your security program.

Every business decision should be driven by data—and cyber security is no exception. In Data-Driven Cybersecurity, you'll master the art and science of quantifiable cybersecurity, learning to harness data for enhanced threat detection, response, and mitigation. You’ll turn raw data into meaningful intelligence, better evaluate the performance of your security teams, and proactively address the vulnerabilities revealed by the numbers.

Data-Driven Cybersecurity will teach you how to:

• Align a metrics program with organizational goals
• Design real-time threat detection dashboards
• Predictive cybersecurity using AI and machine learning
• Data-driven incident response
• Apply the ATLAS methodology to reduce alert fatigue
• Create compelling metric visualizations

Data-Driven Cybersecurity teaches you to implement effective, data-driven cybersecurity practices—including utilizing AI and machine learning for detection and prediction. Throughout, the book presents security as a core part of organizational strategy, helping you align cyber security with broader business objectives. If you’re a CISO or security manager, you’ll find the methods for communicating metrics to non-technical stakeholders invaluable.

Foreword by Joseph Steinberg.

About the technology

A data-focused approach to cybersecurity uses metrics, analytics, and automation to detect threats earlier, respond faster, and align security with business goals.

About the book

Data-Driven Cybersecurity shows you how to turn complex security metrics into evidence-based security practices. You’ll learn to define meaningful KPIs, communicate risk to stakeholders, and turn complex data into clear action. You’ll begin by answering the important questions: what makes a “good” security metric? How can I align security with broader business objectives? What makes a robust data-driven security management program? Python scripts and Jupyter notebooks make collecting security data easy and help build a real-time threat detection dashboards. You’ll even see how AI and machine learning can proactively predict cybersecurity incidents!

What's inside

• Improve your alert system using the ATLAS framework
• Elevate your organization’s security posture
• Statistical and ML techniques for threat detection
• Executive buy-in and strategic investment

About the reader

For readers familiar with the basics of cybersecurity and data analysis.

About the author

Mariano Mattei is a professor at Temple University and an information security professional with over 30 years of experience in cybersecurity and AI innovation.

Table of Contents

Part 1 Building the foundation
1 Introducing cybersecurity metrics
2 Cybersecurity analytics toolkit
3 Implementing a security metrics program
4 Integrating metrics into business strategy
Part 2 The metrics that matter
5 Establishing the foundation
6 Foundations of cyber risk
7 Protecting your assets
8 Continuous threat detection
9 Incident management and recovery
Part 3 Beyond the basics: Advanced analytics, machine learning and AI
10 Advanced cybersecurity metrics
11 Advanced statistical analysis
12 Advanced machine learning analysis
13 Generative AI in cybersecurity metrics