Automated exclusion of personally identifiable information (pii) from crowd-sourced input to artificial intelligence (ai) model of an integration platform

Abstract

State-of-the-art techniques for detecting personally identifiable information (PII) in data-processing workflows are not scalable, are inefficient, and/or are prone to human error. Accordingly, disclosed embodiments utilize a PII classifier during the design phase or at compile time, for an integration process, to automatically determine the likelihood that each field in the integration code of the integration process is personally identifiable information. Fields identified as likely representing PII data may be automatically excluded from being indexed into the crowd-sourced historical data that are used to train an artificial intelligence (AI) model. This improves operational efficiency, scalability, flexibility, data quality, and accuracy of the AI model, as well as facilitating compliance with data privacy and security regulations, improving trust and adoption, and fostering proactive data management.