In the previous post, I provided examples of some controls-related data that can’t be used to support automated cyber risk quantification (CRQ). But the news isn’t all bad. There are some data that can be used to support CRQ.
Jack Jones
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I covered a lot of ground in the previous posts, and rather than summarize them here I’ll assume you’ve read those posts already. So, let’s dive into the last analytic dimension…
In the previous two posts, I briefly discussed that:
- The CRQ market is rapidly growing, and there’s a strong desire to automate CRQ analysis...
In Part 1 of this series, I discussed that the market for cyber risk quantification (particularly automated CRQ) is growing rapidly, but that automation, done poorly, can to more harm than good. In this post, I’ll begin to discuss what it takes to automate CRQ responsibly.
Until recently, it’s mostly been organizations with visionary and early adopter tendencies who have embraced cyber risk quantification (CRQ). They understood the value and were willing to deal with the challenges.
If you search the FAIR Institute blog, you will find several posts about Inherent Risk, each highlighting fundamental problems with the standard definition for Inherent Risk and offering insights and advice regarding how to better define and use it. To save you the trouble of finding and reading old posts, I’ll boil them down:
A study sponsored by the National Science Foundation and reported in the Harvard Business Review, Research: Why Employees Violate Cybersecurity Policies, identified a wide disconnect between the demands of cybersecurity and the reality of day-to-day work for employees – one of the key gaps that the new FAIR Controls Analytics Model™ (FAIR-CAM™) is intended to help close.
In writing the FAIR-CAM™ white paper, I took a short detour from the complex landscape of cybersecurity to explain the new FAIR Controls Analytics Model™ with an analogy that almost anyone can relate to.
Introduced at the October, 2021, FAIR Conference, the FAIR Controls Analytics Model™ (FAIR-CAM™) will begin to have an impact in 2022. Although eventually it should benefit the risk management profession in many ways, both large and small, its effects are likely to be gradual as people and the industry as a whole begins to wrap their minds around its implications.
The Apache Log4j security vulnerability uncovered recently is every cybersecurity defender’s nightmare - a zero-day exploited in a practically ubiquitous software library. Because zero-day exploits aren’t going away anytime soon, it’s important for organizations to increase their resilience to this type of change in the risk landscape.
