Insight generation using personal identifiable information (PII) footprint modeling
Aspects of the disclosure relate to information masking. A computing platform may receive, from a user computing device, a request to access information that includes personal identifiable information (PII). The computing platform may retrieve source data comprising the PII and mask, within the source data and based on a data management policy, the PII. The computing platform may send the masked information in response to the request to access the information. The computing platform may receive a request to unmask the masked information and unmask the PII. The computing platform may log the request to unmask the masked information in an unmasking event log and send the unmasked PII in response to the request to unmask the masked information. The computing platform may apply a machine learning model to the unmasking event log to identify malicious events and trigger remediation actions based on identification of the malicious events.
1. A computing platform comprising:
at least one processor;
a communication interface communicatively coupled to the at least one processor; and
memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:
receive, from a user computing device, a request to access information that includes personal identifiable information (PII);
retrieve source data comprising the PII;
mask, within the source data and based on at least one enterprise data management policy, the PII, resulting in masked information;
send the masked information in response to the request to access the information;
receive a request to unmask the masked information;
unmask the PII, resulting in unmasked PII;
log the request to unmask the masked information in an unmasking event log;
send the unmasked PII in response to the request to unmask the masked information;
apply at least one machine learning model to the unmasking event log to identify one or more malicious events; and
trigger one or more remediation actions based on identification of the one or more malicious events, wherein identifying the one or more malicious events comprises:
identifying that a number of requests for the PII by the user computing device exceeds a median number of requests for the PII by a predetermined number of standard deviations, wherein the requests are initiated by other user computing devices corresponding to users associated with a particular job title and wherein a user of the user computing device may also be associated with the particular job title.
2. The computing platform of claim 1 , wherein masking the PII comprises masking, based on one or more of: an IP address, a location, or a job title corresponding to a user of the user computing device or the user computing device.
3. The computing platform of claim 1 , wherein unmasking the PII comprises sending the corresponding source data that includes the PII.
4. The computing platform of claim 1 , wherein unmasking the PII comprises modifying the masked information to expose the PII.
5. The computing platform of claim 1 , wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:
identify, based on a network policy, whether or not the request to unmask the PII should be fulfilled, wherein unmasking the PII is in response to identifying that the request to unmask the PII should be fulfilled.
6. The computing platform of claim 5 , wherein triggering the one or more remediation actions includes modifying the network policy.
7. The computing platform of claim 6 , wherein modifying the network policy comprises revoking access permissions for the user computing device.
8. The computing platform of claim 7 , wherein the access permissions for the user computing device are revoked for a temporary period of time.
9. The computing platform of claim 1 , wherein identifying the one or more malicious events comprises:
comparing the PII to information that relates to a job title of the user of the user computing device;
based on identifying a match between the PII and the information that relates to the job title of the user of the user computing device, verifying a non-malicious event; and
based on identifying that the PII does not match the information that relates to the job title of the user of the user computing device, identifying the one or more malicious events.
10. A method comprising:
at a computing platform comprising at least one processor, a communication interface, and memory:
receiving, from a user computing device, a request to access information that includes personal identifiable information (PII);
retrieving source data comprising the PII;
masking, within the source data and based on at least one enterprise data management policy, the PII, resulting in masked information;
sending the masked information in response to the request to access the information;
receiving a request to unmask the masked information;
unmasking the PII, resulting in unmasked PII;
logging the request to unmask the masked information in an unmasking event log;
sending the unmasked PII in response to the request to unmask the masked information;
applying at least one machine learning model to the unmasking event log to identify one or more malicious events; and
triggering one or more remediation actions based on identification of the one or more malicious events, wherein identifying the one or more malicious events comprises:
identifying that a number of requests for the PII by the user computing device exceeds a median number of requests for the PII by a predetermined number of standard deviations, wherein the requests are initiated by other user computing devices corresponding to users associated with a particular job title and wherein a user of the user computing device may also be associated with the particular job title.
11. The method of claim 10 , wherein masking the PII comprises masking, based on one or more of: an IP address, a location, or a job title corresponding to a user of the user computing device or the user computing device.
12. The method of claim 10 , wherein unmasking the PII comprises sending the corresponding source data that includes the PII.
13. The method of claim 10 , wherein unmasking the PII comprises modifying the masked information to expose the PII.
14. The method of claim 10 , further comprising:
identifying, based on a network policy, whether or not the request to unmask the PII should be fulfilled, wherein unmasking the PII is in response to identifying that the request to unmask the PII should be fulfilled.
15. The method of claim 14 , wherein triggering the one or more remediation actions includes modifying the network policy.
16. The method of claim 15 , wherein modifying the network policy comprises revoking access permissions for the user computing device.
17. The method of claim 16 , wherein the access permissions for the user computing device are revoked for a temporary period of time.
18. The method of claim 10 , wherein identifying the one or more malicious events comprises:
comparing the PII to information that relates to a job title of the user of the user computing device;
based on identifying a match between the PII and the information that relates to the job title of the user of the user computing device, verifying a non-malicious event; and
based on identifying that the PII does not match the information that relates to the job title of the user of the user computing device, identifying the one or more malicious events.
19. One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:
receive, from a user computing device, a request to access information that includes personal identifiable information (PII);
retrieve source data comprising the PII;
mask, within the source data and based on at least one enterprise data management policy, the PII, resulting in masked information;
send the masked information in response to the request to access the information;
receive a request to unmask the masked information;
unmask the PII, resulting in unmasked PII;
log the request to unmask the masked information in an unmasking event log;
send the unmasked PII in response to the request to unmask the masked information;
apply at least one machine learning model to the unmasking event log to identify one or more malicious events; and
trigger one or more remediation actions based on identification of the one or more malicious events, wherein identifying the one or more malicious events comprises:
identifying that a number of requests for the PII by the user computing device exceeds a median number of requests for the PII by a predetermined number of standard deviations, wherein the requests are initiated by other user computing devices corresponding to users associated with a particular job title and wherein a user of the user computing device may also be associated with the particular job title.