Risk matrix


A risk matrix is a matrix that is used during risk assessment to define the level of risk by considering the category of probability or likelihood against the category of consequence severity. This is a simple mechanism to increase visibility of risks and assist management decision making.
Risk is the lack of certainty about the outcome of making a particular choice. Statistically, the level of downside risk can be calculated as the product of the probability that harm occurs multiplied by the severity of that harm. In practice, the risk matrix is a useful approach where either the probability or the harm severity cannot be estimated with accuracy and precision.
Although standard risk matrices exist in certain contexts, 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:
NegligibleMarginalCriticalCatastrophic
CertainHighHighExtremeExtreme
LikelyModerateHighHighExtreme
PossibleLowModerateHighExtreme
UnlikelyLowLowModerateExtreme
RareLowLowModerateHigh

The company or organization then would calculate what levels of risk they can take with different events. This would be done by weighing the risk of an event occurring against the cost to implement safety and the benefit gained from it.

Example matrix

The following is an example matrix of possible personal injuries, with particular accidents allocated to appropriate cells within the matrix:
NegligibleMarginalCriticalCatastrophic
CertainStubbing Toe
LikelyFall
PossibleMajor Car Accident
UnlikelyAircraft crash
RareMajor Tsunami

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 out that since 61% of cyber security 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 to reinvent well-established quantitative methods used in many equally complex problems."