How to Measure Anything in Cybersecurity Risk

Table of contents

1. Foreword
   1. Note
2. Foreword
3. Acknowledgments
4. About the Authors
5. Introduction
   1. Why This Book, Why Now?
   2. What Is This Book About?
   3. What to Expect
   4. Is This Book for Me?
   5. We Need More Than Technology
   6. New Tools for Decision Makers
   7. Our Path Forward
6. PART I: Why Cybersecurity Needs Better Measurements for Risk
   1. Chapter 1: The One Patch Most Needed in Cybersecurity
      1. The Global Attack Surface
      2. The Cyber Threat Response
      3. A Proposal for Cybersecurity Risk Management
      4. Notes
   2. Chapter 2: A Measurement Primer for Cybersecurity
      1. The Concept of Measurement
      2. The Object of Measurement
      3. The Methods of Measurement
      4. Notes
   3. Chapter 3: Model Now!: An Introduction to Practical Quantitative Methods for Cybersecurity
      1. A Simple One-for-One Substitution
      2. The Expert as the Instrument
      3. Doing “Uncertainty Math”
      4. Visualizing Risk
      5. Supporting the Decision: A Return on Mitigation
      6. Where to Go from Here
      7. Notes
   4. Chapter 4: The Single Most Important Measurement in Cybersecurity
      1. The Analysis Placebo: Why We Can’t Trust Opinion Alone
      2. How You Have More Data Than You Think
      3. When Algorithms Beat Experts
      4. Tools for Improving the Human Component
      5. Summary and Next Steps
      6. Notes
   5. Chapter 5: Risk Matrices, Lie Factors, Misconceptions, and Other Obstacles to Measuring Risk
      1. Scanning the Landscape: A Survey of Cybersecurity Professionals
      2. What Color Is Your Risk? The Ubiquitous—and Risky—Risk Matrix
      3. Exsupero Ursus and Other Fallacies
      4. Conclusion
      5. Notes
7. PART II: Evolving the Model of Cybersecurity Risk
   1. Chapter 6: Decompose It: Unpacking the Details
      1. Decomposing the Simple One-for-One Substitution Model
      2. More Decomposition Guidelines: Clear, Observable, Useful
      3. A Hard Decomposition: Reputation Damage
      4. Conclusion
      5. Notes
   2. Chapter 7: Calibrated Estimates: How Much Do You Know Now?
      1. Introduction to Subjective Probability
      2. Calibration Exercise
      3. Further Improvements on Calibration
      4. Conceptual Obstacles to Calibration
      5. The Effects of Calibration
      6. Notes
      7. Answers to Trivia Questions for Calibration Exercise
   3. Chapter 8: Reducing Uncertainty with Bayesian Methods
      1. A Major Data Breach Example
      2. A Brief Introduction to Bayes and Probability Theory
      3. Bayes Applied to the Cloud Breach Use Case
      4. Note
   4. Chapter 9: Some Powerful Methods Based on Bayes
      1. Computing Frequencies with (Very) Few Data Points: The Beta Distribution
      2. Decomposing Probabilities with Many Conditions
      3. Reducing Uncertainty Further and When To Do It
      4. Leveraging Existing Resources to Reduce Uncertainty
      5. Wrapping Up Bayes
      6. Notes
8. PART III: Cybersecurity Risk Management for the Enterprise
   1. Chapter 10: Toward Security Metrics Maturity
      1. Introduction: Operational Security Metrics Maturity Model
      2. Sparse Data Analytics
      3. Functional Security Metrics
      4. Security Data Marts
      5. Prescriptive Analytics
      6. Notes
   2. Chapter 11: How Well Are My Security Investments Working Together?
      1. Addressing BI Concerns
      2. Just the Facts: What Is Dimensional Modeling and Why Do I Need It?
      3. Dimensional Modeling Use Case: Advanced Data Stealing Threats
      4. Modeling People Processes
   3. Chapter 12: A Call to Action: How to Roll Out Cybersecurity Risk Management
      1. Establishing the CSRM Strategic Charter
      2. Organizational Roles and Responsibilities for CSRM
      3. Getting Audit to Audit
      4. What the Cybersecurity Ecosystem Must Do to Support You
      5. Can We Avoid the Big One?
9. Appendix A: Selected Distributions
   1. Distribution Name: Triangular
   2. Distribution Name: Binary
   3. Distribution Name: Normal
   4. Distribution Name: Lognormal
   5. Distribution Name: Beta
   6. Distribution Name: Power Law
   7. Distribution Name: Truncated Power Law
10. Appendix B: Guest Contributors
    1. Appendix B Contents
    2. Aggregating Data Sources for Cyber Insights
    3. Forecasting—and Reducing—Occurrence of Espionage Attacks
    4. Skyrocketing Breaches?
    5. Financial Impact of Breaches
    6. The Flaw of Averages in Cyber Security
    7. Botnets
    8. Password Hacking
    9. Cyber-CI
    10. How Catastrophe Modeling Can Be Applied to Cyber Risk
    11. Notes
11. Index
12. EULA