Book Image

How to Measure Anything in Cybersecurity Risk

By : Douglas W. Hubbard, Richard Seiersen
Book Image

How to Measure Anything in Cybersecurity Risk

By: Douglas W. Hubbard, Richard Seiersen

Overview of this book

How to Measure Anything in Cybersecurity Risk exposes the shortcomings of current “risk management” practices, and offers a series of improvement techniques that help you fill the holes and ramp up security. In his bestselling book How to Measure Anything, author Douglas W. Hubbard opened the business world’s eyes to the critical need for better measurement. This book expands upon that premise and draws from The Failure of Risk Management to sound the alarm in the cybersecurity realm. Some of the field’s premier risk management approaches actually create more risk than they mitigate, and questionable methods have been duplicated across industries and embedded in the products accepted as gospel. This book sheds light on these blatant risks and provides alternate techniques that can help improve your current situation. You’ll also learn which approaches are too risky to save and are actually more damaging than a total lack of any security. Dangerous risk management methods abound; there is no industry more critically in need of solutions than cybersecurity. This book provides solutions where they exist and advises when to change tracks entirely.
Table of Contents (12 chapters)
Free Chapter
About the Authors

Chapter 9
Some Powerful Methods Based on Bayes

If one fails to specify the prior information, a problem of inference is just as ill-posed as if one had failed to specify the data . . . In realistic problems of inference, it is typical that we have cogent prior information, highly relevant to the question being asked; to fail to take it into account is to commit the most obvious inconsistency of reasoning, and it may lead to absurd or dangerously misleading results.

—Edwin T. Jaynes

Recall that in our survey, 23% of respondents agreed with the statement “Probabilistic methods are impractical because probabilities need exact data to be computed and we don’t have exact data.” This is just a minority, but even those who rejected that claim probably have found themselves in situations where data seemed too sparse to make a useful inference. In fact that may be why the majority of the survey takers also responded that ordinal scales have a place in...