Systems and methods for controlling data exposure using artificial-intelligence-based periodic modeling
Systems and methods for periodically modifying data privacy elements are provided. The systems and methods may identify a set of data privacy elements. A data privacy element can characterizes a feature of a computing device and can be detectable by a network host. A first artificial profile can be generated by modifying a first data privacy element based on an artificial profile model that defines a relationship associated with one or more constraints between the set of data privacy elements. Subsequent to generating the first artificial profile, a second artificial profile can be generated by periodically modifying a second data privacy element in accordance with the relationship defined by the artificial profile model. The computer device can be masked from being identified by the network host by sending the second artificial profile including the second data privacy element to a requested network location.
1. A computer-implemented method, comprising:
gathering seeding data, wherein the seeding data is gathered from a computing environment associated with an organization, and wherein the seeding data includes data privacy elements corresponding to different users associated with the organization;
identifying an organization specific signature associated with the organization, wherein the organization specific signature is based on the seeding data;
training an artificial profile model using a data set of data privacy elements and organization specific signatures, wherein the artificial profile model is trained to define relationships that are associated with different constraints amongst different data privacy elements and the organization specific signature;
generating an artificial profile for a computing device within the computing environment, wherein the artificial profile is generated by modifying a data privacy element associated with the computing device and the organization specific signature, and wherein the artificial profile is generated according to one or more constraints associated with the relationships defined by the artificial profile model;
receiving a signal indicating that the computing device is requesting access to a network location; and
masking the computing device from being identified by a network host by sending the artificial profile including the modified data privacy element and the modified organization specific signature to the network location.
2. The computer-implemented method of claim 1 , wherein the modified data privacy element and the modified organization specific signature allow for identification of the organization while masking the computing device from being identified.
3. The computer-implemented method of claim 1 , wherein the artificial profile is inserted into an isolated virtual environment that monitors for suspicious activity.
4. The computer-implemented method of claim 1 , further comprising:
periodically modifying a second data privacy element to generate a second artificial profile, wherein the second data privacy element is modified in accordance with the one or more constraints.
5. The computer-implemented method of claim 1 , wherein the artificial profile model is trained based on one or more attribution vectors, and wherein an attribution vector represents a detectable characteristic associated with a set of data privacy elements that have been clustered based on a similarity in values.
6. The computer-implemented method of claim 1 , wherein the seeding data corresponds to organizational policies and objectives associated with the organization.
7. The computer-implemented method of claim 1 , wherein the organization specific signature corresponds to computing device configurations for computing devices associated with the different users.
8. A system, comprising:
one or more data processors; and
a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations including:
gathering seeding data, wherein the seeding data is gathered from a computing environment associated with an organization, and wherein the seeding data includes data privacy elements corresponding to different users associated with the organization;
identifying an organization specific signature associated with the organization, wherein the organization specific signature is based on the seeding data;
training an artificial profile model using a data set of data privacy elements and organization specific signatures, wherein the artificial profile model is trained to define relationships that are associated with different constraints amongst different data privacy elements and the organization specific signature;
generating an artificial profile for a computing device within the computing environment, wherein the artificial profile is generated by modifying a data privacy element associated with the computing device and the organization specific signature, and wherein the artificial profile is generated according to one or more constraints associated with the relationships defined by the artificial profile model;
receiving a signal indicating that the computing device is requesting access to a network location; and
masking the computing device from being identified by a network host by sending the artificial profile including the modified data privacy element and the modified organization specific signature to the network location.
9. The system of claim 8 , wherein the modified data privacy element and the modified organization specific signature allow for identification of the organization while masking the computing device from being identified.
10. The system of claim 8 , wherein the artificial profile is inserted into an isolated virtual environment that monitors for suspicious activity.
11. The system of claim 8 , wherein the instructions further cause the one or more data processors to perform additional operations including:
periodically modifying a second data privacy element to generate a second artificial profile, wherein the second data privacy element is modified in accordance with the one or more constraints.
12. The system of claim 8 , wherein the artificial profile model is trained based on one or more attribution vectors, and wherein an attribution vector represents a detectable characteristic associated with a set of data privacy elements that have been clustered based on a similarity in values.
13. The system of claim 8 , wherein the seeding data corresponds to organizational policies and objectives associated with the organization.
14. The system of claim 8 , wherein the organization specific signature corresponds to computing device configurations for computing devices associated with the different users.
15. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause a data processing apparatus to perform operations including:
gathering seeding data, wherein the seeding data is gathered from a computing environment associated with an organization, and wherein the seeding data includes data privacy elements corresponding to different users associated with the organization;
identifying an organization specific signature associated with the organization, wherein the organization specific signature is based on the seeding data;
training an artificial profile model using a data set of data privacy elements and organization specific signatures, wherein the artificial profile model is trained to define relationships that are associated with different constraints amongst different data privacy elements and the organization specific signature;
generating an artificial profile for a computing device within the computing environment, wherein the artificial profile is generated by modifying a data privacy element associated with the computing device and the organization specific signature, and wherein the artificial profile is generated according to one or more constraints associated with the relationships defined by the artificial profile model;
receiving a signal indicating that the computing device is requesting access to a network location; and
masking the computing device from being identified by a network host by sending the artificial profile including the modified data privacy element and the modified organization specific signature to the network location.
16. The computer-program product of claim 15 , wherein the modified data privacy element and the modified organization specific signature allow for identification of the organization while masking the computing device from being identified.
17. The computer-program product of claim 15 , wherein the artificial profile is inserted into an isolated virtual environment that monitors for suspicious activity.
18. The computer-program product of claim 15 , wherein the instructions further cause the data processing apparatus to perform additional operations including:
periodically modifying a second data privacy element to generate a second artificial profile, wherein the second data privacy element is modified in accordance with the one or more constraints.
19. The computer-program product of claim 15 , wherein the artificial profile model is trained based on one or more attribution vectors, and wherein an attribution vector represents a detectable characteristic associated with a set of data privacy elements that have been clustered based on a similarity in values.
20. The computer-program product of claim 15 , wherein the seeding data corresponds to organizational policies and objectives associated with the organization.
21. The computer-program product of claim 15 , wherein the organization specific signature corresponds to computing device configurations for computing devices associated with the different users.