A risk is the amount of harm that can be expected to occur during a given time period due to specific harm event (e.g., an accident). Statistically, the level of risk can be calculated as the product of the probability that harm occurs (e.g., that an accident happens) multiplied by the severity of that harm (i.e., the average amount of harm or more conservatively the maximum credible amount of harm). In practice, the amount of risk is usually categorized into a small number of levels because neither the probability nor harm severity can typically be estimated with accuracy and precision.
Contents
A Risk matrix is a matrix that is used during risk assessment to define the various levels of risk as the product of the harm probability categories and harm severity categories. This is a simple mechanism to increase visibility of risks and assist management decision making.
Although many standard risk matrices exist in different contexts (US DoD, NASA, ISO), individual projects and organizations may need to create their own or tailor an existing risk matrix.
For example, the harm severity can be categorized as:
The probability of harm occurring might be categorized as 'Certain', 'Likely', 'Possible', 'Unlikely' and 'Rare'. However it must be considered that very low probabilities may not be very reliable.
The resulting Risk Matrix could be:
The company or organization then would calculate what levels of Risk they can take with different events. This would be done by weighing up the risk of an event occurring against the cost to implement safety and the benefit gained from it.
An example
The following is an example risk matrix with particular accidents allocated to appropriate cells within the matrix:
Problems
In his article 'What's Wrong with Risk Matrices?', Tony Cox argues that risk matrices experience several problematic mathematical features making it harder to assess risks. These are:
Thomas, Bratvold, and Bickel demonstrate that risk matrices produce arbitrary risk rankings. Rankings depend upon the design of the risk matrix itself, such as how large the bins are and whether or not one uses an increasing or decreasing scale. In other words, changing the scale can change the answer.
Douglas W. Hubbard and Richard Seiersen take the general research from Cox, Thomas, Bratvold, and Bickel, and provide specific discussion in the realm of cybersecurity risk. They point at that since 61% of cybersecurity professionals use some form of risk matrix, this can be a serious problem. Hubbard and Seiersen consider these problems in the context of other measured human errors and conclude that “The errors of the experts are simply further exacerbated by the additional errors introduced by the scales and matrices themselves. We agree with the solution proposed by Thomas et al. There is no need for cybersecurity (or other areas of risk analysis that also use risk matrices) to reinvent well-established quantitative methods used in many equally complex problems.”