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: Normal

Graph: horizontal axis of 0-5 million has bell-shaped curve originates at 0, ascends for one to four million then descends for five million. 90% area marked between one to four million.

Figure A.3 Normal 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.

A normal (or Gaussian) distribution is a bell-shaped curve that is symmetrically distributed about the mean.

  1. Many natural phenomena follow this distribution but in some applications it will underestimate the probability of extreme events.
  2. Empirical rule: Nearly all data points (99.7%) will lie within three standard deviations of the mean.
    • When to Use: When there is equal probability of observing a result above or below the mean.
    • Examples: Test scores, travel time.
    • Excel Formula: =norminv(rand(),(UB + LB)/2,(UB-LB)/3.29)
    • Mean: =((UB + LB)/2)