What factor most influences the choice of abstraction level in an analysis?

The choice of abstraction level in a FAIR analysis is primarily influenced by the quality of the data available. High-quality, relevant, and accurate data allows analysts to create detailed models that capture the nuances of the risk being assessed. When data quality is high, it enhances the reliability of the analysis and the conclusions drawn from it, leading to a more precise risk assessment.

Conversely, if data quality is low, it may necessitate a higher level of abstraction to avoid drawing inaccurate or misleading conclusions. Analysts might simplify the complexities of the risk being evaluated in order to compensate for the lack of reliable data. Therefore, the quality of the data directly affects how much detail and specificity can be reasonably included in the analysis, making it a critical factor in determining the appropriate abstraction level.

While other factors like the number of attack vectors, the size of the threat population, and secondary risks can influence the analysis, they do not have as direct an impact on the choice of abstraction level as data quality does.