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Product details
File Size: 4272 KB
Print Length: 275 pages
Publisher: Wiley; 1 edition (July 25, 2016)
Publication Date: July 25, 2016
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B01J4XYM16
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Amazon Best Sellers Rank:
#63,275 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
Highly recommend this book for anyone who works specifically in Risk Management. For all of us who were taught in formal education to create Risk Matrices using ordinal scales (High/Med/Low) for risk...we were wrong! As a professional field, we need to correct our ways and take advice from actuarial science to do so. Insurance and other business entities have already solved this problem - it's time we use their techniques to solve ours in Cybersecurity Risk.Great work by the authors by moving beyond theory and working to make the advice as practical as possible for the rest of us. Concepts introduced in the book can be put into play on the job tomorrow.Side note: Don't be intimidated by the stats-heavy portions of the book. The authors and editors have done well to dumb these concepts down enough for the rest of us. Aside from perhaps 3 pages, there is very little math required to implement most of the advice in this book.
I was assigned this as one of the texts for a graduate-level seminar in cybersecurity and cyberwarfare economic risk analysis. This book is remarkable in that it presents a clear framework for "non-mathies" to become statistically literate enough to debunk common misconceptions and move beyond the standard qualitative "stoplight chart" style risk matrix charts into true quantifiable probabilities. The authors hold the readers hand each step of the way, beginning with a simple 3-step process to easily replace the standard stoplight risk matrix with actual quantifiable numbers.Fundamental points made by the authors include:- Experts who claim some elements are purely qualitative and cannot be measured are simply wrong and haven't properly defined what they are trying to measure ye.- "We don't have enough information to measure this" is a statement that refutes itself, because it claims there IS some threshold of measurement beyond which it can be "measured" -- implying it can be measured now since it can be compared to that imaginary threshold.- Virtually everything we encounter in any situation has already been measured and has math models for predicting behavior, we just need to figure out what we are trying to measure and find the models for it.- Claiming "there aren't enough samples for statistical significance" shows the person doesn't understand statistics -- a LOT of useful info can be gleamed from very small samples, and all we need to do is REDUCE uncertainty to be useful, not eliminate it.The authors guide the read through the entire process of building a gut-level intuition for basic statistical and probabilistic thinking and modeling, allowing readers to immediately stop using vague "hi/med/low" assessments (that are just as full of errors as any mathematical formulation) and start using quantifiable predictions that can be easily improved as more information becomes available.A great leader once told me that we typically only have about 70% of the information we want to have when the time comes to make a decision. This book helps you increase that number before decision time runs out.
Cybersecurity is frequently criticized for its lack of scientific rigor. This book provides scientific rigor to decision making and strategy within cybersecurity through a solid approach to quantitative risk analysis. By using examples and common tools, Hubbard shows how to apply probability concepts easily to solve questions that many businesses have regarding cyber security: How do I assess high impact / low occurrence events? How can I see the benefit of my investment in cybersecurity controls? How much is reasonable to invest in cybersecurity controls given my appetite for risk? Where should I invest to minimize my cybersecurity risks? If you are looking for a robust and scientific approach to cybersecurity risk analysis, this is the book for you.
Well written and detailed enough for geeks that need another way of looking at risk measurement. The standards of "high, medium, and low" risks are meaningless in most cases. This book is the first one that tries to get a more measurable and consistent view of how to relate risk that senior management can work with.
Absolutely essential for participants in any risk management program who want to get beyond faking things up with 3 level matrices. Quantitative risk analysis requires accuracy, but not absolute precision. This book gives great practical examples and training for getting to as much accuracy as you need for a given application. Study it, and make better decisions for your program.
This was an eye opener for me. Applying statistics can bring a lot of value to cybersecurity and this book presents good examples on how to use it. I would highly recommend for cybersecurity professionals but you might want to brush up your statistic background to fully benefit from the topics covered here.
Interesting read, and very relevant to calculating Risk. I just wish he would have gone deeper into how to actually implement these models.
Outstanding book. Walks you through going from qualitative assessments to applying quantitative rigor to cyber risk assessments. These methods (shown) brings cyber risk assessment and management more into conformance with standard risk management practices.
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