liveBook · Manning

What you will learn in Data-Driven Cybersecurity

Reducing risk with proven metrics

by Mariano Mattei
Foreword by Joseph Steinberg

Chapter by chapter

Chapter 1: Introducing cybersecurity metrics

1.1 Understanding metrics · 1.2 Significance of metrics in cybersecurity · 1.3 Traditional vs. innovative metrics · 1.4 The changing landscape of cybersecurity · 1.5 The importance of frameworks in cybersecurity · 1.5.1 HITRUST framework · 1.5.2 Center for Internet Security CIS Top 18 framework · 1.5.3 NIST Cybersecurity Framework v2.0 · 1.6 AI and predictive cybersecurity metrics · 1.7 Defending against AI · 1.8 Summary

What you learn in chapter 1 →

Chapter 2: Cybersecurity analytics toolkit

2.1 Tool selection · 2.1.1 Key tool selection factors · 2.2 Dashboard development · 2.2.1 Choosing the right metrics · 2.2.2 Knowledge points · 2.2.3 Dashboard example · 2.3 Statistical analysis · 2.3.1 Trend analysis · 2.3.2 Correlation analysis · 2.3.3 Probability distribution · 2.4 Integrated security analytics environment · 2.4.1 Integrated security analytics environment exercise · 2.5 Continuous improvement and iteration · 2.5.1 Iteration techniques · 2.5.2 Continuous improvement cycle · 2.6 Summary

What you learn in chapter 2 →

Chapter 3: Implementing a security metrics program

3.1 Introducing metrics program design · 3.1.1 Key components of a security metrics program · 3.2 METRICS methodology · 3.2.1 Measure · 3.2.2 Evaluate · 3.2.3 Threshold · 3.2.4 Report · 3.2.5 Improve · 3.2.6 Communicate · 3.2.7 Sustain · 3.3 Using the METRICS methodology example · 3.3.1 Step 1: Measure · 3.3.2 Step 2: Evaluate · 3.3.3 Step 3: Threshold setup · 3.3.4 Step 4: Report · 3.3.5 Step 5: Improve · 3.3.6 Step 6: Communicate · 3.3.7 Step 7: Sustain · 3.3.8 Exercise summary · 3.4 Building a metrics dashboard · 3.4.1 Different dashboards for different stakeholders · 3.4.2 Security dashboard example · 3.5 Tools and technologies for metrics collection · 3.5.1 Open source tools for metrics collection · 3.5.2 Commercial tools for metrics collection · 3.5.3 Automation and integration · 3.5.4 Scripting for metrics collection · 3.6 Common pitfalls in metrics programs · 3.6.1 Pitfall 1: Overreliance on quantitative metrics · 3.6.2 Pitfall 2: Misalignment with business goals · 3.6.3 Pitfall 3: Stagnation and complacency · 3.7 Summary

What you learn in chapter 3 →

Chapter 4: Integrating metrics into business strategy

4.1 Business alignment · 4.1.1 Business-aligned security metrics example · 4.1.2 Mapping metrics to business performance · 4.1.3 Supporting innovation with metrics · 4.2 Security metrics alignment with business strategy exercise · 4.2.1 Step 1: Understanding MediHealth’s business objectives · 4.2.2 Step 2: Selecting key metrics · 4.2.3 Step 3: Mapping metrics to business goals · 4.2.4 Step 4: Measuring metrics · 4.2.5 Step 5: Communicating the Metrics · 4.3 Security metric reporting · 4.3.1 Presenting metrics in executive reports · 4.3.2 Demonstrating return on investment · 4.3.3 Communication strategies · 4.3.4 Metrics communication exercise · 4.4 Summary

What you learn in chapter 4 →

Chapter 5: Establishing the foundation

5.1 Governance · 5.2 Organizational context · 5.2.1 Understanding the differences between mission, vision, and values · 5.2.2 Strategic objectives · 5.2.3 Organizational metrics · 5.2.4 Organizational metrics exercise · 5.3 Risk management strategy · 5.3.1 Risk mitigation · 5.3.2 Risk management metrics · 5.3.3 Risk management metrics exercise · 5.4 Roles, responsibilities, and authorities · 5.4.1 Roles, responsibilities, and authorities metrics · 5.4.2 Roles and responsibilities metrics exercise · 5.5 Policy, processes, and procedures · 5.5.1 Policy, processes, and procedures metrics · 5.5.2 Policy, processes, and procedures metrics exercise · 5.6 Oversight · 5.6.1 Governance structure · 5.6.2 Simulated governance structure · 5.6.3 Governance exercise · 5.6.4 Oversight metrics · 5.6.5 Oversight metrics exercise · 5.7 Cybersecurity supply chain risk management · 5.7.1 Supply chain risk management practices · 5.7.2 Cybersecurity supply chain risk management metrics · 5.7.3 Supply chain risk management metrics exercise · 5.8 Governance metrics · 5.9 Answer to exercise 5.6.3 · 5.10 Summary

What you learn in chapter 5 →

Chapter 6: Foundations of cyber risk

6.1 Identify · 6.2 Asset management · 6.2.1 Asset management metrics · 6.2.2 Asset management metrics exercise · 6.3 Risk assessments · 6.3.1 NIST 800-30: Guide for conducting risk assessments · 6.3.2 Risk assessment metrics · 6.3.3 Risk assessment metrics exercise · 6.4 Continuous improvement · 6.4.1 Continuous improvement metrics · 6.5 Identity metrics exercise · 6.6 Summary

What you learn in chapter 6 →

Chapter 7: Protecting your assets

7.1 Identity management, authentication, and access control · 7.1.1 Authentication · 7.1.2 Access control · 7.1.3 Identity and credential management · 7.1.4 Context-based identity proofing and credential binding · 7.1.5 User, service, and hardware authentication · 7.1.6 Identity assertion, protection, conveyance, and verification · 7.1.7 Access permissions management · 7.1.8 Physical access controls · 7.1.9 Types of security controls · 7.1.10 Identity management, authentication, and access control metrics · 7.1.11 Identity and access management dashboard exercise · 7.2 Awareness and training · 7.2.1 Awareness and training metrics · 7.2.2 Awareness and training metrics exercise · 7.3 Data security · 7.3.1 Data at rest · 7.3.2 Data in transit · 7.3.3 Data in use · 7.3.4 Data backup and recovery · 7.3.5 Data security metrics · 7.3.6 Data security metrics exercise · 7.4 Configuration management · 7.4.1 Software maintenance · 7.4.2 Hardware maintenance · 7.4.3 Log maintenance · 7.4.4 Unauthorized software prevention · 7.4.5 Secure software development · 7.4.6 Platform metrics · 7.4.7 Platform security metrics exercise · 7.5 SDLC testing exercise · 7.6 Technology infrastructure resilience · 7.6.1 Networks · 7.6.2 Environmental threats · 7.6.3 Resilience mechanisms · 7.6.4 Resource capacity · 7.7 Protection metrics · 7.8 Answer to exercise from section 7.5 · 7.9 Summary

What you learn in chapter 7 →

Chapter 8: Continuous threat detection

8.1 Implementing continuous threat monitoring systems · 8.1.1 Defining strategy · 8.1.2 Establishing architecture, implementing data collection, and analysis · 8.1.3 Responding to findings · 8.2 Open source alternative to continuous threat detection · 8.3 Continuous monitoring metrics · 8.3.1 Continuous monitoring metrics exercise · 8.4 Understanding ATLAS · 8.4.1 ATLAS methodology · 8.4.2 Review and update · 8.4.3 ATLAS benefits · 8.5 Determining valid threat detections · 8.5.1 False rejection rate · 8.5.2 False acceptance rate · 8.5.3 Equal error rate · 8.5.4 FRR, FAR, and EER metrics · 8.5.5 FRR, FAR, and ERR exercise · 8.6 Adverse event analysis · 8.6.1 Adverse event analysis metrics · 8.6.2 Adverse event analysis metrics exercise · 8.7 Alternatives to ATLAS and Wazuh · 8.8 Summary

What you learn in chapter 8 →

Chapter 9: Incident management and recovery

9.1 Incident management · 9.2 Planning and preparation · 9.3 Testing an IRP · 9.3.1 Tabletop exercise example · 9.3.2 Tabletop exercise metrics · 9.3.3 Table for tabletop exercise evaluation sample · 9.4 Detection and documentation · 9.4.1 Three-tier severity model · 9.4.2 Five-tier severity model · 9.4.3 Changing severity over time · 9.4.4 Practical example of incident documentation · 9.4.5 Chain of custody · 9.4.6 Incident management metrics · 9.4.7 Incident management metrics exercise · 9.5 Incident triage and analysis · 9.5.1 Case study: Financial institution data breach · 9.5.2 Incident metrics · 9.5.3 Incident metrics exercise · 9.5.4 Incident metrics table · 9.6 Notification and communication · 9.7 Containing validated incidents · 9.8 Evidence gathering and forensic analysis · 9.9 Eradication of incidents · 9.9.1 Incident response metrics · 9.10 Recovering to operational status · 9.11 Post-incident and lessons learned · 9.11.1 Incident reporting and communication metrics · 9.11.2 Incident reporting and communication metrics exercise · 9.12 Summary

What you learn in chapter 9 →

Chapter 10: Advanced cybersecurity metrics

10.1 Risk exposure and predictive analysis · 10.2 Risk exposure and predictive metrics · 10.2.1 Risk exposure index · 10.2.2 Predictive threat index · 10.2.3 Risk exposure and predictive metrics exercise · 10.2.4 Using open source tools to calculate REI and PTI · 10.2.5 How cybersecurity teams work together with open source solutions · 10.3 Advanced threat detection · 10.3.1 Anomaly detection · 10.3.2 Time to predict · 10.3.3 Dynamic risk scoring · 10.3.4 Advanced threat detection metric exercise · 10.4 Effectiveness of AI in cybersecurity · 10.4.1 False-positive suppression rate · 10.4.2 AI-based decision accuracy rate · 10.4.3 Effectiveness of AI in cybersecurity exercise · 10.5 Cloud and network threat management · 10.5.1 Cloud threat detection · 10.5.2 Cloud threat detection accuracy · 10.5.3 Attack surface index · 10.5.4 Cloud and network threat management metric exercise · 10.6 Incident management · 10.6.1 Threat hunting · 10.6.2 Incident impact estimation · 10.6.3 Incident management metric exercise · 10.7 Continuous improvement · 10.7.1 Risk mitigation · 10.7.2 Time to predict · 10.7.3 Continuous improvement metric exercise · 10.8 Summary

What you learn in chapter 10 →

Chapter 11: Advanced statistical analysis

11.1 Continuous improvement with statistical metrics · 11.1.1 Key concepts for continuous improvement · 11.1.2 Free resources for statistical analysis · 11.2 Using statistical metrics for continuous improvement · 11.3 Implementing a feedback loop · 11.4 Finding hidden relationships in data · 11.5 Using moving averages to track trends · 11.5.1 Moving averages example · 11.5.2 Detailed description of the plots · 11.6 Grouping similar events for better insights · 11.7 Forecasting cybersecurity trends · 11.7.1 ARIMA in action · 11.8 Bayesian inference · 11.8.1 Interpreting the results · 11.9 Statistical models for vulnerability management · 11.9.1 Interpreting the results · 11.10 Summary

What you learn in chapter 11 →

Chapter 12: Advanced machine learning analysis

12.1 Code requirements · 12.1.1 What is AI · 12.2 DBSCAN for threat detection · 12.2.1 DBSCAN example · 12.3 Random forest and SVM for threat detection · 12.3.1 Random forest and SVM exercise · 12.3.2 Isolation forest and autoencoders exercise · 12.4 Natural language processing · 12.4.1 NLP exercise · 12.5 Deep learning · 12.5.1 Deep learning exercise · 12.6 Reinforcement learning · 12.6.1 Reinforcement learning example in Python code · 12.7 Summary

What you learn in chapter 12 →

Chapter 13: Generative AI in cybersecurity metrics

13.1 Understanding generative AI · 13.2 Open source generative AI alternatives · 13.2.1 LM Studio · 13.2.2 Ollama · 13.2.3 LM Studio vs. Ollama · 13.2.4 Why we chose LM Studio for this chapter · 13.3 A note on LLMs · 13.4 Prompt engineering · 13.4.1 Why prompt engineering matters · 13.5 Prompt engineering in cybersecurity · 13.5.1 Basics of prompt engineering · 13.5.2 Open source vs. cloud API integration · 13.6 Generating and analyzing cybersecurity data · 13.6.1 Generating synthetic cybersecurity data · 13.6.2 Analyzing the output · 13.7 Enhancing reporting and visualization · 13.7.1 Failed login reasons reporting · 13.8 Automating incident response and trend analysis · 13.8.1 Setting the context · 13.9 Exploring generative AI · 13.9.1 Learning resources · 13.9.2 Inspiration to experiment · 13.9.3 Your next steps · 13.10 Limitations and ethical considerations · 13.11 Summary

What you learn in chapter 13 →

Concepts explained in this book

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.