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One example method includes detecting a threat in a data confidence fabric, assigning a data confidence score to data implicated by the threat, generating trust insertion metadata concerning the threat, creating a ledger entry based on the data confidence score and the trust insertion metadata, and using the ledger entry to determine an overall data confidence score for the data confidence fabric. A data threat portfolio view is generated based on the data confidence score and the trust insertion metadata, and the data threat portfolio view is presented to a user.

Titel:	VARIABLE DCF SECURITY SCORES AND DATA THREAT PORTFOLIO VIEWS
Link:	View record in USPTO Patent Applications (Volltext)
Veröffentlichung:	2021
Medientyp:	Patent
Sonstiges:	Nachgewiesen in: USPTO Patent Applications Sprachen: English Document Number: 20210409436 Publication Date: December 30, 2021 Appl. No: 16/917042 Application Filed: June 30, 2020 Claim: 1. A method, comprising: detecting a threat in a data confidence fabric; assigning a data confidence score to data implicated by the threat; generating trust insertion metadata concerning the threat; creating a ledger entry based on the data confidence score and the trust insertion metadata; and using the ledger entry to determine an overall data confidence score for the data confidence fabric. Claim: 2. The method as recited in claim 1, wherein the threat is detected by a security technology running on a gateway of the data confidence fabric. Claim: 3. The method as recited in claim 1, wherein the overall data confidence score is also based on trust insertion metadata generated by one or more devices in the data confidence fabric other than a device that detected the threat. Claim: 4. The method as recited in claim 1, wherein the entity that assigns the data confidence score is configured to assign both integer, and non-integer, data confidence scores. Claim: 5. The method as recited in claim 1, further comprising adjusting the data confidence score. Claim: 6. The method as recited in claim 1, wherein the data is generated and/or collected by an IoT device. Claim: 7. The method as recited in claim 1, wherein the trust insertion metadata is added to a trust insertion metadata vector, and the data confidence score is added to a trust insertion range vector, and the overall data confidence score of the data confidence fabric is based on the trust insertion range vector and the trust insertion metadata vector. Claim: 8. The method as recited in claim 1, further comprising generating a data threat portfolio view based on the data confidence score and the trust insertion metadata. Claim: 9. The method as recited in claim 1, further comprising presenting a graphical display of a data threat portfolio view that was generated based on the data confidence score and the trust insertion metadata. Claim: 10. The method as recited in claim 1, wherein the data passes through an entity that detects the threat. Claim: 11. A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising: detecting a threat in a data confidence fabric; assigning a data confidence score to data implicated by the threat; generating trust insertion metadata concerning the threat; creating a ledger entry based on the data confidence score and the trust insertion metadata; and using the ledger entry to determine an overall data confidence score for the data confidence fabric. Claim: 12. The non-transitory storage medium as recited in claim 11, wherein the threat is detected by a security technology running on a gateway of the data confidence fabric. Claim: 13. The non-transitory storage medium as recited in claim 11, wherein the overall data confidence score is also based on trust insertion metadata generated by one or more devices in the data confidence fabric other than a device that detected the threat. Claim: 14. The non-transitory storage medium as recited in claim 11, wherein the entity that assigns the data confidence score is configured to assign both integer, and non-integer, data confidence scores. Claim: 15. The non-transitory storage medium as recited in claim 11, wherein the operations further comprise adjusting the data confidence score. Claim: 16. The non-transitory storage medium as recited in claim 11, wherein the data is generated and/or collected by an IoT device. Claim: 17. The non-transitory storage medium as recited in claim 11, wherein the trust insertion metadata is added to a trust insertion metadata vector, and the data confidence score is added to a trust insertion range vector, and the overall data confidence score of the data confidence fabric is based on the trust insertion range vector and the trust insertion metadata vector. Claim: 18. The non-transitory storage medium as recited in claim 11, wherein the operations further comprise generating a data threat portfolio view based on the data confidence score and the trust insertion metadata. Claim: 19. The non-transitory storage medium as recited in claim 11, wherein the operations further comprise presenting a graphical display of a data threat portfolio view that was generated based on the data confidence score and the trust insertion metadata. Claim: 20. The non-transitory storage medium as recited in claim 11, wherein the data passes through an entity that detects the threat. Current International Class: 04; 04

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VARIABLE DCF SECURITY SCORES AND DATA THREAT PORTFOLIO VIEWS