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How to Measure Anything in Cybersecurity Risk

How to Measure Anything in Cybersecurity Risk

By : Douglas W. Hubbard, Richard Seiersen
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How to Measure Anything in Cybersecurity Risk

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

Distribution Name: Triangular

Graph: horizontal axis of 0-5 million has line ascending up to point between 3-4 million, descending to 5 million forming triangular shape. 90% of area marked for graph.

Figure A.1 Triangular Distribution

Parameters:

  • UB (Upper bound)
  • LB (Lower bound)
  • Mode—this may be any value between UB and LB.

Note that UB and LB are absolute outer limits—a 100% CI.

For a triangular distribution, the UB and LB represent absolute limits. There is no chance that a value could be generated outside of these bounds. In addition to the UB and LB, this distribution also has a mode that can vary to any value between the UB and LB. This is sometimes useful as a substitute for a lognormal, when you want to set absolute limits on what the values can be but you want to skew the output in a way similar to a lognormal. It is useful in any situation where you know of absolute limits but the most likely value might not be in the middle, like the normal distribution.

  • When to Use: When you want control over where the most likely value is compared to the range, and when the range has absolute limits.
  • Examples: Number of records lost...
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How to Measure Anything in Cybersecurity Risk
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