Secure generative-artificial intelligence platform integration on a cloud service

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 actions including:
• accessing a model metadata and a set of weights of a generative machine-learning model encrypted using a first encryption key that is generated by a generative machine-learning platform, wherein the generative machine-learning model is associated with the generative machine-learning platform hosted on a cloud, and wherein the model metadata includes the first encryption key;
• utilizing the first encryption key from the model metadata to decrypt, based on the model metadata, the set of weights of the generative machine-learning model;
• generating a second encryption key within a cloud home region;
• encrypting, based on the model metadata, the set of weights by the second encryption key;
• storing the encrypted set of weights to a cloud storage in the cloud home region;
• accessing the encrypted set of weights from the cloud storage;
• decrypting the encrypted set of weights using the second encryption key;
• loading the set of weights from the cloud storage to a temporary storage;
• receiving an inference query associated with the generative machine-learning model into the temporary storage;
• performing an inference task by applying the set of weights to the inference query;
• generating an inference output in response to performing the inference task;
• and
• deleting the set of weights from the temporary storage.

A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions including:

• accessing a model metadata and a set of weights of a generative machine-learning model encrypted using a first encryption key that is generated by a generative machine-learning platform, wherein the generative machine-learning model is associated with the generative machine-learning platform hosted on a cloud, and wherein the model metadata includes the first encryption key;
• utilizing the first encryption key from the model metadata to decrypt, based on the model metadata, the set of weights of the generative machine-learning model;
• generating a second encryption key within a cloud home region;
• encrypting, based on the model metadata, the set of weights by the second encryption key;
• storing the encrypted set of weights to a cloud storage in the cloud home region;
• accessing the encrypted set of weights from the cloud storage;
• decrypting the encrypted set of weights using the second encryption key;
• loading the set of weights from the cloud storage to a temporary storage;
• receiving an inference query associated with the generative machine-learning model into the temporary storage;
• performing an inference task by applying the set of weights to the inference query;
• generating an inference output in response to performing the inference task;
• and
• deleting the set of weights from the temporary storage.