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商品描述
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 Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.
- 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 Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.
商品描述(中文翻譯)
衡量、改善並傳達您的安全計畫的價值。
每一個商業決策都應該以數據為驅動力，而網路安全也不例外。在數據驅動的網路安全中，您將掌握可量化網路安全的藝術與科學，學會利用數據來增強威脅檢測、回應和緩解。您將把原始數據轉化為有意義的情報，更好地評估您的安全團隊的表現，並主動解決數字所揭示的漏洞。 數據驅動的網路安全將教您如何： - 將指標計畫與組織目標對齊- 設計實時威脅檢測儀表板
- 使用AI和機器學習進行預測性網路安全
- 數據驅動的事件回應
- 應用ATLAS方法論以減少警報疲勞
- 創建引人注目的指標可視化 數據驅動的網路安全教您實施有效的數據驅動網路安全實踐，包括利用AI和機器學習進行檢測和預測。整本書將安全視為組織策略的核心部分，幫助您將網路安全與更廣泛的商業目標對齊。如果您是CISO或安全經理，您會發現向非技術利益相關者傳達指標的方法非常寶貴。 前言由Joseph Steinberg撰寫。 關於技術 一種以數據為中心的網路安全方法使用指標、分析和自動化來更早檢測威脅、更快回應，並將安全與商業目標對齊。 關於本書 數據驅動的網路安全向您展示如何將複雜的安全指標轉化為基於證據的安全實踐。您將學會定義有意義的KPI，向利益相關者傳達風險，並將複雜數據轉化為清晰的行動。您將開始回答重要問題：什麼是「良好」的安全指標？我如何將安全與更廣泛的商業目標對齊？什麼構成一個穩健的數據驅動安全管理計畫？Python腳本和Jupyter筆記本使收集安全數據變得簡單，並幫助建立實時威脅檢測儀表板。您甚至會看到AI和機器學習如何主動預測網路安全事件！ 內容概覽 - 使用ATLAS框架改善您的警報系統
- 提升您組織的安全姿態
- 用於威脅檢測的統計和機器學習技術
- 高層支持和戰略投資 關於讀者 適合熟悉網路安全和數據分析基礎的讀者。 關於作者 Mariano Mattei是天普大學的教授，並且是一位擁有超過30年網路安全和AI創新經驗的信息安全專業人士。 目錄 第一部分 建立基礎
1 介紹網路安全指標
2 網路安全分析工具包
3 實施安全指標計畫
4 將指標整合進商業策略
第二部分 重要的指標
5 建立基礎
6 網路風險的基礎
7 保護您的資產
8 持續威脅檢測
9 事件管理與恢復
第三部分 超越基礎：進階分析、機器學習和AI
10 進階網路安全指標
11 進階統計分析
12 進階機器學習分析
13 生成式AI在網路安全指標中的應用 購買印刷版書籍時，您將獲得Manning提供的免費電子書（PDF或ePub），以及訪問在線liveBook格式（及其AI助手，將以任何語言回答您的問題）的權限。
作者簡介
Mariano Mattei is VP of Cybersecurity and AI at Azzur Solutions with over 30 years of software engineering experience. As a Certified Chief Information Security Officer (CCISO), he specializes in AI integration for advanced threat detection and predictive security measures within the biotechnology, pharmaceuticals, and medical device sectors.
作者簡介(中文翻譯)
Mariano Mattei 是 Azzur Solutions 的網路安全與人工智慧副總裁，擁有超過 30 年的軟體工程經驗。作為一名認證首席資訊安全官 (CCISO)，他專注於在生物技術、製藥和醫療設備領域中，進行人工智慧整合以實現先進的威脅檢測和預測性安全措施。
