Hybrid cognitive system for ai/ml data privacy

A hybrid cognitive system comprising:

• at least one private cloud node having confidential data stored thereon, the at least one private cloud configured to train a second model using at least one machine learning technique on the confidential data, the at least one private cloud configured to export the second model to a public cloud;
• a public cloud node configured to run a first model trained on at least one machine learning technique on data from the public cloud, and configured to run a second model, the public cloud further including computer readable instructions effective to cause the public cloud to:
• receive a query by a virtual assistant running on the public cloud;
• determine whether the query pertains to data available on the public cloud, or the query pertains to the confidential data available on the private cloud;
• interpret the query by using the first model on the public cloud when it is determined that the query pertains to the data available on the public cloud;
• and
• interpret the query by the using the second model when it is determined that the query pertains to the confidential data available on the private cloud.

A method comprising:

• receiving a query by a virtual assistant running on a public cloud;
• determining whether the query pertains to data available on a public cloud resource, or the query pertains to data available on a private cloud resource;
• interpreting the query by using a first model trained on at least one machine learning technique on data from the public cloud when it is determined that the query pertains to the data available on the public cloud resource;
• and
• interpreting the query by the using a second model trained on at least one machine learning technique on data from the private cloud when it is determined that the query pertains to the data available on the private cloud resource.