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
1
Foreword
2
Foreword
3
Acknowledgments
4
About the Authors
9
Index
10
EULA

Distribution Name: Lognormal

Graph: horizontal axis of 0-5 million has bell-shaped curve originate, rise at 0, descend after two million become stable, end at five million. 90% area marked between 0-3 million.

Figure A.4 Lognormal Distribution

Parameters:

  • UB (Upper bound)
  • LB (Lower bound)

Note that LB and UB in the Excel formula below represent a 90% CI. There is a 5% chance of being above the UB and a 5% chance of being below the LB.

The lognormal distribution is an often preferred alternative to the normal distribution when a sample can only take positive values. Consider the expected future value of a stock price. In the equation S1 = S0e(r), S1 is the future stock price, S0 is the present stock price, and r is the expected rate of return. The expected rate of return follows a normal distribution and may very well take a negative value. The future price of a stock, however, is bounded at zero. By taking the exponent of the normally distributed expected rate of return, we will generate a lognormal distribution where a negative rate may have an adverse effect on the future stock price, without ever leading the stock price below the zero bound. It also allows...