Granted Patent B2

US 12,008,134 · App. 17/332,467 · Granted Jun 11, 2024

Inventors: Shibi Panikkar (Bangalore, IN); Thiru Shama (Bangalore, IN); Chetan Pudiyanda Somaiah (Bangalore, IN)

Assignee: Dell Products L.P.

G06F21/6245G06F21/31G06F21/604G06N20/00

Abstract

Claims (44)

1. An apparatus comprising:

at least one processing device comprising a processor coupled to a memory, the at least one processing device, when executing program code, is configured to:

download a dataset from a data source;

detect whether or not the dataset contains at least a subset of data of a given security-based data type, wherein the detection is performed in accordance with an artificial intelligence model;

generate a machine-computed security level classification for the dataset by automatically selecting one of a plurality of security level classifications based on the detection;

compare the machine-computed security level classification with a user-generated security level classification manually selected from the plurality of security level classifications;

cause one or more actions to be taken based on the comparison; and

create a unique identifier for the dataset to track a location of the dataset, the unique identifier comprising a user identification and a timestamp;

wherein, when the generated machine-computed security level classification is a restricted security level classification, the one or more actions comprise relocating the dataset based on the unique identifier from a device that downloaded the dataset to a secure storage location.

2. The apparatus of claim 1 , wherein, when the comparison results in the machine-computed security level classification being more restricted than the user-generated security level classification, the one or more actions comprise causing a review of the dataset.

3. The apparatus of claim 2 , wherein, following the review, the one or more actions comprise changing a current security level classification of the dataset.

4. The apparatus of claim 1 , wherein, when the comparison results in the machine-computed security level classification being less restricted than the user-generated security level classification, the one or more actions comprise retraining the artificial intelligence model.

5. The apparatus of claim 1 , wherein, when the machine-computed security level classification is one of one or more restricted security level classifications, the one or more actions comprise deleting the dataset from a device that downloaded the dataset.

6. The apparatus of claim 1 , wherein, when the machine-computed security level classification is one of one or more restricted security level classifications, the one or more actions comprise encrypting the dataset.

7. The apparatus of claim 1 , wherein, when the comparison results in the machine-computed security level classification being the same as the user-generated security level classification, the one or more actions comprise continuously monitoring a current security level classification of the dataset.

8. The apparatus of claim 1 , wherein the one or more actions comprise tracking a location of the dataset.

9. The apparatus of claim 1 , wherein the one or more actions comprise applying a severity level to the dataset based on the comparison, wherein the severity level is automatically selected from a plurality of severity levels and represents a degree of need for review of the dataset.

10. The apparatus of claim 1 , wherein the given security-based data type comprises personally identifiable information.

11. The apparatus of claim 1 , wherein the given security-based data type comprises entity-sensitive information.

12. A method comprising:

downloading a dataset from a data source;

detecting whether or not the dataset contains at least a subset of data of a given security-based data type, wherein the detection is performed in accordance with an artificial intelligence model;

generating a machine-computed security level classification for the dataset by automatically selecting one of a plurality of security level classifications based on the detection;

comparing the machine-computed security level classification with a user-generated security level classification manually selected from the plurality of security level classifications;

causing one or more actions to be taken based on the comparison; and

creating a unique identifier for the dataset to track a location of the dataset, the unique identifier comprising a user identification and a timestamp;

wherein, when the generated machine-computed security level classification is a restricted security level classification, the one or more actions comprise relocating the dataset based on the unique identifier from a device that downloaded the dataset to a secure storage location.

13. The method of claim 12 , wherein, when the comparison results in the machine-computed security level classification being more restricted than the user-generated security level classification, the one or more actions comprise causing a review of the dataset.

14. The method of claim 12 , wherein, when the comparison results in the machine-computed security level classification being less restricted than the user-generated security level classification, the one or more actions comprise retraining the artificial intelligence model.

15. The method of claim 12 , wherein, when the machine-computed security level classification is one of one or more restricted security level classifications, the one or more actions comprise at least one of:

deleting the dataset from a device that downloaded the dataset; and

encrypting the dataset.

16. The method of claim 12 , wherein, when the comparison results in the machine-computed security level classification being the same as the user-generated security level classification, the one or more actions comprise continuously monitoring a current security level classification of the dataset.

17. The method of claim 12 , wherein the one or more actions comprise tracking a location of the dataset.

18. The method of claim 12 , wherein the one or more actions comprise applying a severity level to the dataset based on the comparison, wherein the severity level is automatically selected from a plurality of severity levels and represents a degree of need for review of the dataset.

19. A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device cause the at least one processing device to:

download a dataset from a data source;

detect whether or not the dataset contains at least a subset of data of a given security-based data type, wherein the detection is performed in accordance with an artificial intelligence model;

generate a machine-computed security level classification for the dataset by automatically selecting one of a plurality of security level classifications based on the detection;

compare the machine-computed security level classification with a user-generated security level classification manually selected from the plurality of security level classifications;

cause one or more actions to be taken based on the comparison; and

create a unique identifier for the dataset to track a location of the dataset, the unique identifier comprising a user identification and a timestamp;

wherein, when the generated machine-computed security level classification is a restricted security level classification, the one or more actions comprise relocating the dataset based on the unique identifier from a device that downloaded the dataset to a secure storage location.

20. The computer program product of claim 19 , wherein, when the comparison results in the machine-computed security level classification being more restricted than the user-generated security level classification, the one or more actions comprise causing a review of the dataset.

Assignments (8)

Continuity (1)