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Likely Decision Points and Relevant Data

Feedback Welcome

We made every effort to put forward informed and useful decision frameworks with wide applicability, but the goal of this documentation is more to solicit feedback than make a declaration. We welcome questions, constructive criticism, refuting evidence, or supporting evidence about any aspect of this proposal. Please submit feedback to the GitHub repository.

We propose the following decision points and associated values should be a factor when making decisions about vulnerability prioritization. We emphasize that these descriptions are hypotheses to be further tested and validated.

We propose satisfactory decision points for vulnerability management in the next sections, in alphabetical order. Each decision point page includes advice on gathering information about the decision point. SSVC using Current Information Sources provides some suggestions about how existing sources of information about vulnerabilities can be used to collate responses to these decision points.

Decision Point Values are Ordered Sets

The values for each decision point are ordered sets, meaning that the order of the values is significant. The ordering of the values is intended to reflect the relative importance of the values, with the first value being the least important and the last value being the most important. By requiring ordered sets, we can apply consistency checks that ensure that the outcome priority of a set of decision point values is greater than or equal to the outcome priority of another set of decision point values if the first set of values is greater than or equal to the second set of values in every dimension.

What determines the ordering?

The relevant dimension to which the ordering for both decision points and outcomes applies can be different for different decision points and outcomes. Sometimes this is a "better" or "worse" dimension, but it seems to generalize to a "more likely to act" or "less likely to act" of dimension.

Where are the Unknown options?

One important omission from the values for each category is an unknown option. Instead, we recommend explicitly identifying an option that is a reasonable assumption based on prior events. Such an option requires reliable historical evidence for what tends to be the case; of course, future events may require changes to these assumptions over time. Therefore, our assumptions require evidence and are open to debate in light of new evidence. Different risk tolerance or risk discounting postures are not addressed in the current work; accommodating such tolerance or discounting explicitly is an area for future work. This flexibility fits into our overall goal of supplying a decision-making framework that is both transparent and fits the needs of different communities. Resisting an unknown option discourages the modeler from silently embedding these assumptions in their choices for how the decision tree flows below the selection of any unknown option.