Error prediction using database validation rules and machine learning

A non-transitory computer readable medium having instructions stored thereon that, when executed by a processor, cause the processor to permit limited access to a user's secure information, wherein, when executed, the instructions cause the processor to:

• define a plurality of validation rules comprising business logic for validating transactions performed on a first database, of a plurality of databases, comprising a data model;
• perform transactions using the first database, wherein the transactions comprise one or more predefined database operations, and the first database comprises a post-transaction state after performance of the transactions;
• apply a subset of the validation rules to the first database in the post-transaction state, wherein a portion of the applied validation rules successfully validate and at least one of the applied validation rules fails to validate;
• predict, based on the at least one failed validation rule and using a first trained machine learning model, one or more errors with respect to the first database for one or more future transactions;
• and
• generate, using a second trained machine learning model, a suggested database solution for the at least one failed validation rule.

A system for permitting limited access to a user's secure information, the system comprising:

• a processor;
• and
• a memory storing instructions for execution by the processor, the instructions configuring the processor to:
• define a plurality of validation rules comprising business logic for validating transactions performed on a first database, of a plurality of databases, comprising a data model;
• perform transactions using the first database, wherein the transactions comprise one or more predefined database operations, and the first database comprises a post-transaction state after performance of the transactions;
• apply a subset of the validation rules to the first database in the post-transaction state, wherein a portion of the applied validation rules successfully validate and at least one of the applied validation rules fails to validate;
• predict, based on the at least one failed validation rule and using a first trained machine learning model, one or more errors with respect to the first database for one or more future transactions;
• and
• generate, using a second trained machine learning model, a suggested database solution for the at least one failed validation rule.