Better Security Metrics with the Clairvoyant Test

"Clairvoyant at Whitby"    by    Snapshooter46    is licensed under    CC BY-NC-SA 2.0

“Clairvoyant at Whitby” by Snapshooter46 is licensed under CC BY-NC-SA 2.0

There’s an apocryphal business quote from Drucker, Demmings, or maybe even Lord Kelvin that goes something like this: “You can’t manage what you don’t measure.” I’ll add that you can’t measure what you don’t clearly define.

Clearly defining the object of measurement is where many security metrics fail. I’ve found one small trick borrowed from the field of Decision Science that helps in the creation and validation of clear, unambiguous, and succinct metrics. It’s called The Clairvoyant Test, and it’s a 30-second thought exercise that makes the whole process quick and easy.

What is the Clairvoyant Test?

The Clairvoyant Test was first introduced in 1975 as a decision analysis tool in a paper titled “Probability Encoding in Decision Analysis” by Spetzler and Von Holstein. It’s intended to be a quick critical thinking tool to help form questions that ensure what we want to measure is, in reality, measurable. It’s easily extended to security metrics by taking the metric description or definition and passing it through the test.

The Clairvoyant Test supposes that one can ask a clairvoyant to gather the metric, and if they are able to fetch it, it is properly formed and defined. In real life, the clairvoyant represents the uninformed observer in your company.

There’s a catch, and this is important to remember: the clairvoyant only has the power of observation.  

The Catch: Qualities of the Clairvoyant

 The clairvoyant can only view events objectively through a crystal ball (or whatever it is clairvoyants use). 

  • They cannot read minds. The clairvoyant’s powers are limited to what can be observed through the crystal ball. You can’t ask the clairvoyant if someone is happy, if training made them smarter, or if they are less likely to reuse passwords over multiple websites.

  • The clairvoyant cannot make judgments. For example, you can’t ask if something is good, bad, effective, or inefficient.

  • They can only observe. Questions posed to the clairvoyant must be framed as observables. If your object of measurement can’t be directly observed, decompose the problem until it can be.

  • They cannot extrapolate. The clairvoyant cannot interpret what you may or not mean, offer conjecture or fill in the gaps of missing information. In other words, they can only give you data.

What’s a well-designed metric that passes the Clairvoyant Test?

A well-designed metric has the following attributes:

  • Unambiguous: The metric is clearly and concisely written; in fact, it is so clear and so concise that there is very little room for interpretation. For example, the number of red cars on Embarcadero St. between 4:45 and 5:45 pm will be interpreted the same way by the vast majority of people.

  • Objective: Metrics avoid subjective judgments, such as “effective” or “significant.” Those words mean different things to different people and can vary greatly across age, experience, cultural, and language backgrounds. 

  • Quantitative: Metrics need to be quantitative measurements. “Rapid deployment of critical security patches” is not quantitative; “Percentage of vulnerabilities with an EPSS probability of 80% of higher remediated within ten days” is.

  • Observable: The metrics need to be designed so that anyone, with the right domain knowledge and access, can directly observe the event you are measuring. 

A few examples…

Let’s try a few common metrics and pass through The Clairvoyant Test to see if they’re measurable and written concisely.

  • Metric: % of users with privileged access

The clairvoyant would not be able to reveal the value of the metric. “Privileged access” is a judgment call and means different things to different people. The clairvoyant would also need to know what system to look into. Let’s rewrite:

  • New Metric: % of users with Domain Admin on the production Active Directory domain

The new metric is objective, clear, and measurable. Additional systems and metrics (root on Linux systems, AWS permissions, etc.) can be aggregated.

Let’s try a metric that is a little harder:

  • Metric: Percentage of vendors with effective cybersecurity policies.

The clairvoyant would not be able to reveal this either – “effective” is subjective, and believe it or not – a cybersecurity policy is not the same across all organizations. Some have a 50-page documented program, others have a 2-page policy, and even others would provide a collection of documents: org chart, related policies, and a 3-year roadmap. Rewritten, “effective” needs to be defined, and “policy” needs to be decomposed. For example, a US-based bank could start with this: 

  • New Metric: % of vendors that have a written and approved cybersecurity policy that adheres to  FFIEC guidelines.

This metric is a good starting point but needs further work – the FFIEC guidelines by themselves don’t pass The Clairvoyant Test, but we’re getting closer to something that does. We can now create an internal evaluation system or scorecard for reviewing vendor security policies. In this example, keep decomposing the problem and defining attributes until it passes The Clairvoyant Test.

Conclusion and Further Work

Do your security metrics pass The Clairvoyant Test? If they don’t, you may have a level of ambiguity that leads to audience misinterpretation. Start with a few metrics and try rewriting them. You will find that clearly stated and defined metrics leads to a security program that is easier to manage.

*** This is a Security Bloggers Network syndicated blog from Blog - Tony Martin-Vegue authored by Tony MartinVegue. Read the original post at: