Secure large language model data gateway
AssigneeBank of America Corporation
InventorVijay Kumar Yarabolu
Life of this patent
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Abstract
Aspects of the disclosure relate to providing a secure large language model data platform. The secure large language model uses a machine-learning large language model and gateway to prevent attacks and unauthorized access to enterprise-managed information and resources. The secure large language model may utilize pre-enrollment at a secure gateway providing a unique identification to each client. A private/public key pair may be generated and stored in the secure gateway database and large language model respectively. In some embodiments, a unique anonymization rule set may be generated and used for each client. Threat actors cannot query the large language model directly based on the pre-enrollment process. Unauthorized requests cannot be decrypted by the large language model due to missing paired keys.
Claims20
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 a natural language request at a secure gateway;
- determine a client identification for the received natural language request;
- based on the determined client identification, determine anonymization rules and an associated private key for client, wherein the private key is stored at the secure gateway and a corresponding public key is stored at the large language model;
- anonymize the natural language request;
- encrypt the anonymized natural language request using the private key;
- transmit the encrypted anonymized natural language request to a large language model for execution of the natural language request;
- at the large language model, decrypt the anonymized natural language request using the public key;
- generate output responsive to the anonymized natural language request;
- transmit generated output to the secure gateway;
- deanonymize the generated output at the secure gateway;
- convert the deanonymized generated output into a natural language output responsive to the natural language request;
- and
- transmit the natural language output responsive to the natural language request.
A method, comprising:
- at a computing platform comprising at least one processor, a communication interface, and memory:
- receive a natural language request at a secure gateway;
- determine a client identification for the received natural language request;
- based on the determined client identification, determine anonymization rules and an associated private key for client, wherein the private key is stored at the secure gateway and a corresponding public key is stored at the large language model;
- anonymize the natural language request;
- encrypt the anonymized natural language request using the private key;
- transmit the encrypted anonymized natural language request to a large language model for execution of the natural language request;
- at the large language model, decrypt the anonymized natural language request using the public key;
- generate output responsive to the anonymized natural language request;
- transmit generated output to the secure gateway;
- deanonymize the generated output at the secure gateway;
- convert the deanonymized generated output into a natural language output responsive to the natural language request;
- and
- transmit the natural language output responsive to the natural language request.
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 a natural language request at a secure gateway;
- determine a client identification for the received natural language request;
- based on the determined client identification, determine anonymization rules and an associated private key for client, wherein the private key is stored at the secure gateway and a corresponding public key is stored at the large language model;
- anonymize the natural language request;
- encrypt the anonymized natural language request using the private key;
- transmit the encrypted anonymized natural language request to a large language model for execution of the natural language request;
- at the large language model, decrypt the anonymized natural language request using the public key;
- generate output responsive to the anonymized natural language request;
- transmit generated output to the secure gateway;
- deanonymize the generated output at the secure gateway;
- convert the deanonymized generated output into a natural language output responsive to the natural language request;
- and
- transmit the natural language output responsive to the natural language request.
Dependencies are inferred from the claim text.