Название: Data-Driven Cybersecurity: Reducing risk with proven metrics (MEAP v5)
Автор: Mariano Mattei
Издательство: Manning Publications
Год: 2025
Страниц: 470
Язык: английский
Формат: pdf, epub
Размер: 24.6 MB

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.

AI is a broad term that encompasses various technologies, including Machine Learning (ML), natural language processing (NLP), and Generative AI with Large Language Models (LLMs). In cybersecurity, AI is leveraged to identify complex patterns and correlations that human analysts might overlook. When trained on high-quality historical and real-time data, Machine Learning models can recognize subtle indicators of compromise, enabling predictive security measures.

However, AI models are only as effective as the data they are trained on and the algorithms that power them. Poor quality, biased, or incomplete data can lead to false positives, missed threats, or misleading insights. Likewise, AI decision-making algorithms must be carefully designed, tested, and validated to avoid errors that could undermine security efforts.

The good news is that you don’t need to build AI algorithms from scratch. Many open-source libraries and frameworks—such as Scikit-learn, TensorFlow, and PyTorch—already provide pre-built models for cybersecurity applications. The key tasks are to collect high-quality, representative data, ensure AI systems are learning the right patterns, and continuously monitor and refine outputs to maintain effectiveness in an evolving threat landscape.

Generative AI isn’t limited to proprietary platforms. Open-source tools like LM Studio and Ollama offer robust solutions for organizations looking to integrate AI while maintaining control over their data and infrastructure. LM Studio is a powerful desktop application designed to run large language
models (LLMs) locally on your machine. It supports popular models like LLaMA, Falcon, and GPT-J, allowing you to fine-tune and query them without relying on external servers. This is particularly useful in industries where data privacy is paramount.

about the book
Data-Driven Cybersecurity shows you the metrics, data, and KPIs that will help you assess and enhance the performance of your cyber security programs. You’ll learn how to turn complex metrics into actionable security practices, following the unique ATLAS (Alert Threshold Lifecycle Assessment System) methodology created by author Mariano Mattei. You’ll go hands-on to build a real-time threat detection dashboard, and discover how AI and machine learning can proactively predict cybersecurity incidents. Case studies, interactive exercises, templates for KPIs, and expert insights help illustrate each new concept.

about the reader
For readers familiar with the basics of cyber security and data analysis.






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