# Probability of Getting Pwnt

I recently finished listening to episode 398 of the Risky Business podcast where Patrick interviews Professor Lawrence Gordon. The discussion is great, as all of Patrick’s shows are, but something caught my attention.  Prof Gordon describes a model he developed many years ago for determining the right level of IT security investment; something that I am acutely interested in.  Professor points out that a key aspect of determining the proper level of investment is the probability of an attack, and he points out that the probability needs to be estimated by the people who know the company in question best: the company’s leadership.

That got me thinking: how do company leaders estimate that probability?  I am sure there are as many ways to do it as there are people doing it, however the discussion reminded me of a key topic in Daniel Kahneman’s book “Thinking Fast and Slow” regarding base rates. Base rates are more or less an average quantity measured against a population for a given concept. For example, the probability of dying in a car crash is about 1 in 470.  That’s the base rate. If I wanted to estimate my likelihood of dying in a car crash, I should start with the base rate and make adjustments I believe are necessary given unique factors to me, such as that I don’t drive to work every day, I don’t drink while driving and so on. So, maybe I end up with my estimate being 1 in 60o.

If i didn’t use a base rate, how would I estimate my likelihood of dying in a car crash?  Maybe I would do something like this:

Probability of Jerry dying in a car crash <

1/(28 years driving x 365 x 2 driving trips per day)

This tells me I have driven about 20,000 times without dying. So, I pin my likelihood of dying in a car crash at less than 1 in 20,000.

But that’s not how it works. The previous 20,000 times I drove don’t have a lot to do with the likelihood of me dying in a car tomorrow, except that I have experience that makes it somewhat less likely I’ll die.  This is why considering base rates are key. If something hasn’t happened to me, or happens really rarely, I’ll assign it a low likelihood. But, if you ask me how likely it is for my house to get robbed right after it got robbed, I am going to overstate the likelihood.

This tells me that things like the Verizon DBIR or the VERIS database are very valuable in helping us define our IT security risk by providing a base rate we can tweak.

I would love to know if anyone is doing this. I have to believe this is already a common practice.