Identifier Recommendations
6 min
the recommended tab on the identifiers page surfaces recommendations based on your existing monitoring setup flare analyzes your current identifiers and suggests additional ones that may be relevant to your organization, helping you close coverage gaps faster this tab displays all suggestions in a table with the following information found from the existing identifier in your tenant that triggered the recommendation, along with the total number of recommendations linked to it types the configure identifiers docid\ bpcmrpdohzer0cc83vcdn being recommended, such as email, azure tenant, domain, and others relevancy scores one or more tags indicating the priority of the recommendations associated with that identifier see relevancy score for details date found the date flare first surfaced the recommendation clicking on an identifier group displays a list of all identifiers within that group along with their associated relevancy scores relevancy score each recommendation includes a relevancy score, which helps you prioritize which suggestions to act on first higher scores indicate recommendations that are more likely to reduce your security exposure the score appears as a tag and can be one of three values low , medium , or high how the score is calculated the score is determined by three factors identifier type the category of the identifier being recommended number of associated events how frequently the identifier has been linked to an event number of associated leaks how many leaks the identifier appears in these factors are combined and mapped to low , medium , or high according to predefined ranges searching and filtering use the search bar to find recommendations by identifier name click filters to open the filter panel, which lets you narrow results by the following criteria status toggle between recommended (active suggestions) and rejected (suggestions you have previously dismissed) types filter by identifier type, such as email, azure tenant, or domain relevancy scores filter by score value ( low , medium , or high ) to focus on the recommendations that matter most related articles