A method for quantitative unified analysis of unstructured threat intelligence data, the method to be performed by a computer system comprising: receiving unstructured threat intelligence data from molecular level sources, wherein the unstructured threat intelligence data comprises security events generated from the molecular level sources; uniting the received unstructured threat intelligence data by translating the received threat intelligence data into a uniformed meta-format and storing the translated data in a database, wherein each translated data includes at least a time component; determining isolated data points from the translated data stored in the database by grouping the translated data into groups, wherein each group represents a unique time period, and applying unsupervised machine learning techniques to each group of the translated data; determining data outliers from the isolated data points; generating a learned function using the data outliers; and providing the generated learned function to at least one of a plurality of upstream sources to update a security rule set of the at least one of the plurality of upstream sources using the provided generated learned function, wherein the updated security rule set is used to filter threats to the at least one of the plurality of upstream sources wherein the determining the isolated data points from the translated data stored in the database comprises: grouping the translated data having time components contained within a first time period into a first group and grouping the translated data having time components contained within a second time period into a second group, wherein the second time period comprises a period of time that is longer than the first time period; clustering the translated data in the first and second groups using unsupervised machine learning techniques; plotting the clusters and identifying clusters having a total number of data points below a predefined threshold; and categorizing the data in the identified clusters as isolated data points. receiving unstructured threat intelligence data from molecular level sources, wherein the unstructured threat intelligence data comprises security events generated from the molecular level sources; uniting the received unstructured threat intelligence data by translating the received threat intelligence data into a uniformed meta-format and storing the translated data in a database, wherein each translated data includes at least a time component; determining isolated data points from the translated data stored in the database by grouping the translated data into groups, wherein each group represents a unique time period, and applying unsupervised machine learning techniques to each group of the translated data; determining data outliers from the isolated data points; generating a learned function using the data outliers; and providing the generated learned function to at least one of a plurality of upstream sources to update a security rule set of the at least one of the plurality of upstream sources using the provided generated learned function, wherein the updated security rule set is used to filter threats to the at least one of the plurality of upstream sources wherein the determining the isolated data points from the translated data stored in the database comprises: grouping the translated data having time components contained within a first time period into a first group and grouping the translated data having time components contained within a second time period into a second group, wherein the second time period comprises a period of time that is longer than the first time period; clustering the translated data in the first and second groups using unsupervised machine learning techniques; plotting the clusters and identifying clusters having a total number of data points below a predefined threshold; and categorizing the data in the identified clusters as isolated data points. grouping the translated data having time components contained within a first time period into a first group and grouping the translated data having time components contained within a second time period into a second group, wherein the second time period comprises a period of time that is longer than the first time period; clustering the translated data in the first and second groups using unsupervised machine learning techniques; plotting the clusters and identifying clusters having a total number of data points below a predefined threshold; and categorizing the data in the identified clusters as isolated data points.