How to Measure Anything in Cybersecurity Risk

This book presents real solutions for assessing cybersecurity risk by skillfully applying the quantitative language of risk analysis to information security. It simplifies the complexity of quantifying uncertainty and sheds light on matters with little data or seemingly intangible goals—and dispels long-held beliefs about cybersecurity practices as well as provides authoritative guidance to solving problems by measuring risk. The book provides practical guide to better risk assessment by describing a very simple quantitative solution, building on it with more advanced methods, and providing detailed advice for choosing the one that best fits the reader’s needs.

New to this edition will be a new case example, some new simple measurement/estimation methods (e.g., pseudo-random number generator and the new methods for combining expert opinion), and a discussion of some objections to quantitative methods. In addition two new chapters will be added: (1) advanced Bayesian methods and (2) practical roll-out of a program from ground zero to maturity.

DOUGLAS W. HUBBARD is the inventor of the Applied Information Economics (AIE) method and the founder of Hubbard Decision Research. He is an internationally recognized expert in the area of decision analysis.

RICHARD SEIERSEN is the Chief Risk Officer of Resilience, a cyberinsurance firm. He is the former Chief Information Security Officer at LendingClub, Twilio, and GE Healthcare and Co-founder of the cloud native security company Soluble – sold to Lacework in 2021.

Foreword for the Second Edition Jack Jones ix

Acknowledgments xiii

Preface xv

Introduction 1

Part I Why Cybersecurity Needs Better Measurements for Risk 5

Chapter 1 The One Patch Most Needed in Cybersecurity 7

Chapter 2 A Measurement Primer for Cybersecurity 21

Chapter 3 The Rapid Risk Audit: Starting With a Simple Quantitative Risk Model 43

Chapter 4 The Single Most Important Measurement in Cybersecurity 73

Chapter 5 Risk Matrices, Lie Factors, Misconceptions, and Other Obstacles to Measuring Risk 101

Part II Evolving the Model of Cybersecurity Risk 133

Chapter 6 Decompose It: Unpacking the Details 135

Chapter 7 Calibrated Estimates: How Much Do You Know Now? 155

Chapter 8 Reducing Uncertainty with Bayesian Methods 183

Chapter 9 Some Powerful Methods Based on Bayes 193

Part III Cybersecurity Risk Management for the Enterprise 231

Chapter 10 Toward Security Metrics Maturity 233

Chapter 11 How Well Are My Security Investments Working Together? 257

Chapter 12 A Call to Action: How to Roll Out Cybersecurity Risk Management 277

Appendix A Selected Distributions 289

Appendix B Guest Contributors 297

Index 327