Systems and methods of determining compromised identity information
A compromised data exchange system extracts data from websites using a crawler, detects portions within the extracted data that resemble personally identifying information (PII) data based on PII data patterns using a risk assessment module, and compares a detected portion to data within a database of disassociated compromised PII data to determine a match using the risk assessment module. A risk score may be assigned to a data item within the database in response to determining the match. In some embodiments, URL data may also be detected in the extracted data. The detected URL data represents further websites that can be automatically crawled by the system to detect further PII data.
1 . A compromised data exchange system, comprising:
a network interface;
one or more processors; and
a memory coupled with the one or more processors, the memory storing instructions thereon that, when executed, cause the one or more processors to:
extract data from one or more websites;
detect portions of the data that resemble personally identifying information (PII) data based on PII data patterns;
compare a detected portion of the data to data within a database of disassociated compromised PII data to determine a match, wherein the disassociated compromised PII data comprises PII data elements that are disconnected from one another and cannot be re-associated to correlate the PII data elements to an actual consumer identity by anyone other than a data originator of the PII data elements; and
assign a risk score to a data item within the database in response to determining the match.
2 . The compromised data exchange system of claim 1 , wherein the instructions further cause the one or more processors to:
provide the detected portions of the data to one or both of an administrator and an artificial intelligence engine.
3 . The compromised data exchange system of claim 1 , wherein the instructions further cause the one or more processors to:
encrypt the portions of the data that resemble PII data, wherein the data within the database of disassociated compromised PII data is encrypted.
4 . The compromised data exchange system of claim 3 , wherein:
the portions of the data that resemble PII data are encrypted using a same set of one or more encryption keys as used to encrypt the data within the database of disassociated compromised PII data.
5 . The compromised data exchange system of claim 4 , wherein:
the one or more encryption keys comprise different encryption keys for each field of PII data.
6 . The compromised data exchange system of claim 1 , wherein the instructions further cause the one or more processors to:
assign a risk score to each piece of disassociated data within the database of disassociated compromised PII data associated with a particular data breach event in response to determining multiple matches between the portions and the disassociated data.
7 . The compromised data exchange system of claim 1 , wherein the instructions further cause the one or more processors to:
increase a risk score for a piece of disassociated data within the database of disassociated compromised PII data in response to determining the match.
8 . A method of analyzing compromised data, comprising:
extracting data from one or more websites;
detecting portions of the data that resemble personally identifying information (PII) data based on PII data patterns;
comparing a detected portion of the data to data within a database of disassociated compromised PII data to determine a match, wherein the disassociated compromised PII data comprises PII data elements that are disconnected from one another and cannot be re-associated to correlate the PII data elements to an actual consumer identity by anyone other than a data originator of the PII data elements; and
assigning a risk score to a data item within the database in response to determining the match.
9 . The method of analyzing compromised data of claim 8 , wherein:
at least one website of the one or more websites is a website that is not indexed on search engines.
10 . The method of analyzing compromised data of claim 9 , wherein:
the at least one website associated with the dark web.
11 . The method of analyzing compromised data of claim 8 , further comprising:
ranking the one or more websites based on a metric of comprised information associated with each of the one or more websites.
12 . The method of analyzing compromised data of claim 11 , wherein:
ranking the one or more websites comprises combining and quantifying extracted patterns within the data from each of the one or more websites.
13 . The method of analyzing compromised data of claim 8 , further comprising:
deploying new data patterns of interested based on changed in breached data posting behavior.
14 . The method of analyzing compromised data of claim 11 , wherein:
the data is extracted using a crawler.
15 . A non-transitory computer-readable medium having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to:
extract data from one or more websites;
detect portions of the data that resemble personally identifying information (PII) data based on PII data patterns;
compare a detected portion of the data to data within a database of disassociated compromised PII data to determine a match, wherein the disassociated compromised PII data comprises PII data elements that are disconnected from one another and cannot be re-associated to correlate the PII data elements to an actual consumer identity by anyone other than a data originator of the PII data elements; and
assign a risk score to a data item within the database in response to determining the match.
16 . The non-transitory computer-readable medium of claim 15 , wherein the instructions further cause the one or more processors to:
identify at least one website associated with one or both of a uniform resource locator and a link from the extracted data.
17 . The non-transitory computer-readable medium of claim 16 , wherein the instructions further cause the one or more processors to:
extract data from the at least one website.
18 . The non-transitory computer-readable medium of claim 16 , wherein the instructions further cause the one or more processors to:
rank websites of the one or more websites from which the one or both of the uniform resource locator and the link associated with each of the at least one website was found.
19 . The non-transitory computer-readable medium of claim 18 , wherein:
ranking the websites of the one or more websites is performed based on a number of different sets of PII data at each of the websites of the one or more websites.
20 . The non-transitory computer-readable medium of claim 15 , wherein:
the risk score is determined at least in part based on a ranking of a webpage from which the data item was extracted.