Granted Patent B1

US 12,105,844 · App. 18/621,791 · Granted Oct 1, 2024

Inventors: Tanner Burns (Austin, TX); Kwesi Cappel (Austin, TX); Kenneth Yeung (Ottawa, CA)

Assignee: HiddenLayer, Inc.

G06F21/6245G06F40/166G06F40/284

Abstract

Claims (47)

1. A computer-implemented method comprising:

receiving, from a proxy intercepting outputs of a generative artificial intelligence (GenAI) model executing in a model computing environment, an output of the GenAI model responsive to a prompt by a requestor;

tokenizing the output to result in a plurality of tokens;

determining, using the tokens, that the output comprises at least one string comprising personally identifiable information (PII), the determining using pattern recognition to identify tokens and sequence of tokens indicative of PII;

assigning, using a classifier, a PII type to each string in the output comprising PII;

determining that at least one of the PII types in the output requires redaction;

redacting the strings having a PII type determined to require redaction to result in a modified output; and

causing the modified output to be transmitted to the requestor.

2. The method of claim 1 , wherein the determination that least one of the PII types in the output requires redaction is based on a policy associated with the requestor.

3. The method of claim 2 , wherein the policy is unique to the requestor and is one of a plurality of different available policies.

4. The method of claim 3 , wherein the policy is one of a plurality of different available policies and is based on a class of users which includes the requestor.

5. The method of claim 1 further comprising:

determining that at least one of the PII types in the output does not requires redaction;

wherein strings corresponding to the PII types not requiring redaction are not modified as part of the modified output.

6. The method of claim 1 , wherein the classifier comprises at least one machine learning model.

7. The method of claim 1 , wherein the GenAI model comprises a large language model.

8. A system comprising:

at least one data processor; and

non-transitory memory storing instructions which, when executed by the at least one data processor, results in operations comprising:

receiving, from a proxy intercepting outputs of a generative artificial intelligence (GenAI) model executing in a model computing environment, an output of the GenAI model responsive to a prompt by a requestor;

tokenizing the output to result in a plurality of tokens;

determining, using the tokens, that the output comprises at least one string comprising personally identifiable information (PII), the determining using pattern recognition to identify tokens and sequence of tokens indicative of PII;

assigning, using a classifier, a PII type to each string in the output comprising PII;

determining that at least one of the PII types in the output requires redaction;

redacting the strings having a PII type determined to require redaction to result in a modified output; and

causing the modified output to be transmitted to the requestor.

9. The system of claim 8 , wherein the determination that least one of the PII types in the output requires redaction is based on a policy associated with the requestor.

10. The system of claim 9 , wherein the policy is unique to the requestor and is one of a plurality of different available policies.

11. The system of claim 10 , wherein the policy is one of a plurality of different available policies and is based on a class of users which includes the requestor.

12. The system of claim 8 , wherein the operations further comprise:

determining that at least one of the PII types in the output does not requires redaction;

wherein strings corresponding to the PII types not requiring redaction are not modified as part of the modified output.

13. The system of claim 8 , wherein the classifier comprises at least one machine learning model.

14. The system of claim 8 , wherein the GenAI model comprises a large language model.

15. A computer-implemented method comprising:

receiving, from a proxy intercepting inputs and outputs of an artificial intelligence (AI) model executing in a model computing environment, a prompt from a requestor for ingestion by the AI model;

determining whether the prompt comprises personally identifiable information (PII);

blocking the prompt for ingestion by the AI model if it is determined that the prompt comprises PII;

receiving, from the proxy, an output of the AI model responsive to the prompt if it is determined that the prompt does not comprise PII;

determining whether the output comprises PII;

allowing the output to be transmitted to the requestor if it is determined that the output does not comprise PII; and

selectively redacting the output before transmission to the requestor based on a policy associated the requestor, the policy specifying levels of PII that require redaction for the requestor.

16. The method of claim 15 , wherein a PII type is assigned to each string in the output comprising PII.

17. The method of claim 16 , wherein the PII type is assigned by a machine learning-based classifier.

18. The method of claim 15 , wherein the AI model is a large language model.

19. The method of claim 15 , wherein the selective redaction is based on a policy associated with the requestor.

20. The method of claim 19 , wherein the policy is one of a plurality of different available policies and is based on a class of users which includes the requestor.

Assignments (1)

Cited By (12)

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