The auditor stared at me blankly. The gaze turned into a gape and lasted long enough to make me shift uncomfortably in my chair, click my pen and look away before I looked back at him.

The blank look flashed to anger.

“Of course, malicious insiders are the biggest threat to this company. They’re the biggest threat to ALL companies.”

He waved the latest copy of a vendor report, which would lead anyone to believe malicious insiders are the single biggest threat to American business since Emma Goldman.

The report he waved in the room was not research at all. It was vendor marketing, thinly disguised as a “survey of Information Security leaders.” It was solely based on an unscientific survey of a small group of people. It reeked of error and bias.

Selection bias is what makes these surveys virtually worthless. I previously wrote about the problems of surveys in information security vendor reports and I want to dig in deeper on a topic from the last post: properly selecting a representative sample from the general population being surveyed. This matters so much. This is perhaps the most important step when conducting a statistically sound survey.

Why this matters

Risk analysts are one of many professions that rely on both internal and external incident data to assess risk. If a risk analyst is performing an assessment of current or former employees stealing customer data, there are two primary places one would look for incident data to determine frequency: internal incident reports and external data on frequency of occurrence.

One of the first places a risk analyst would look would be one of the many published reports on insider threat. The analyst would then find one or several statistics about the frequency of current or former employees stealing data, and use the figure to help provide a likelihood of a loss event.

If the survey is statistically sound, the results can be extrapolated to the general population. In other words, if the survey states that 12% of insiders use USB devices to steal data, within a margin of error, you can use that same range to help inform your assessment.

If the survey is not statistically sound, the results only apply to respondents of the survey. This is called selection bias.

What is selection bias?

There are many forms of bias that are found in statistics, and by extension, in surveys, but the most common is selection bias. It’s the easiest to get wrong and throws the results off the quickest.

Selection bias occurs when the survey result is systematically different from the population that is being studied. Here are a few ways this happens.

• Undercoverage: Underrepresentation of certain groups in the sample. For example, if you are surveying information security professionals, you will want pen testers, risk analysts, department heads, CISO’s — essentially a cross-section. If you have a hard time getting CISO’s to answer the survey, the survey will be biased toward undercoverage of CISO’s.

• Voluntary Response: This occurs when your survey takers are self-selected. The most common example of this is online surveys or polls. Slashdot polls are fun — but completely non-scientific because of voluntary response. Optimally, one would like to have participants randomly selected to ensure a good cross-section of groups of people.

• Participation bias: This occurs when a certain group of participants are more or less likely to participate than others. This can happen when a certain group appreciates the value of surveys more than others (risk analysts versus pen testers) or if survey takers are incentivized, such as with reward points or cash. Compensating survey takers is a very contentious practice and will usually result in people taking the survey that are not in the intended sample population.

Real-world example

There are many to choose from, but I found the “2015 Vormetric Insider Threat Report” from a random Google search. The report is aesthetically polished and, on the surface, very informative. It has the intended effect of making any reader nervous about data theft from employees and contractors.

The report is based on a survey of 818 IT professionals that completed an online survey. The report authors are very careful; they frame the report as opinion of the respondents. Furthermore, there is a short disclosure at the end of the report that states the survey methodology, identifies the company that performed the survey (Harris Poll) and states that the “…online survey is not based on a probability sample and therefore no estimate of theoretical sampling error can be calculated.”

Let me translate that: This report is for marketing and entertainment purposes only.

Why not?

Here’s another problem: Harris Poll compensates their survey takers. Survey takers earn points (called “HIpoints”) for every survey they fill out. These points can be redeemed for gift cards and other items. We already know the survey isn’t statistically significant from the disclosure, but one must ask — can the survey be trusted to include only IT professionals, if the respondents are self-selected and are rewarded if they say anything to qualify for the survey?

The most obvious problem here is voluntary selection and participation bias; both lead to a situation in which you should not use the survey results to base any serious decision on.

I don’t mean to pick on Vormetric exclusively. There are hundreds of similar surveys out there.

Here’s another one. The Cyberthreat Defense Group conducted an online survey that asked many enterprise security questions. One of the results was that 60% of the respondent’s organizations were hit by ransomware in 2016. I fast-forwarded to the section that described the methodology. They vaguely disclosed the fact that survey takers were PAID and the survey results represented the opinions of the respondents. It’s back-page news, but at least it’s there. This is the problem:

Now it’s not the opinion of a small, self-selected, compensated group of people that may or may not be in security. Now it’s fact.

Then it gets tweeted, re-tweeted, liked, whatever. Now it’s InfoSec Folklore.

See the problem?