A Compliance-Driven Framework for Data Curation and Gating in Machine Learning Training for Enterprise Privacy Infrastructure

Abstract

Large-scale​‍​‌‍​‍‌​‍​‌‍​‍‌ machine learning systems spread over distributed infrastructures are confronted with crucial issues of managing sensitive data and, at the same time, abiding by regulatory requirements. In general, training pipelines do not have the means by which they can monitor the way in which protected data is introduced to model development; thus, there are quite significant privacy risks in decentralized environments. Also, the lack of complete visibility hinders the organizations' capability to trace data sources, grasp the movement of information between the systems, and check the conformity to the compliance requirements. In many cases, sensitive data is not properly safeguarded and is even allowed to be exploited beyond authorized purposes, both during training and inference stages. Automated classification systems detect sensitivity indicators within datasets and apply metadata tags specifying permissible uses at the precise moment information feeds into training operations. Gating mechanisms function as policy enforcement points that validate access requests against predefined rules, ensuring models access only data appropriate for declared purposes. Attribute-based access control looks at a variety of factors that include attributes of the subject, classes of the resources as well as certain conditions of the environment, and, based on all these factors, it dynamically makes the decision about the authorization. Machine learning anomaly detection is a kind of vigilant system that constantly watches the access patterns and, through behavioral analysis, it can pinpoint the variations from already established compliance standards. Distributed logging that is supported by blockchain keeps very detailed and at the same time very secure audit trails that enable, in the future, the checking of data usage throughout the lifecycle of the models.