An Experimental Protocol for Explainable AI-Driven Secure Cloud Data Migration Using Synthetic Healthcare Data

Priyanka Nalawade; Prashant Kumbharkar; Pankaj Agarkar; Avanish Kumbharkar; Sudan Jha

doi:10.3791/71612

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Method Article

An Experimental Protocol for Explainable AI-Driven Secure Cloud Data Migration Using Synthetic Healthcare Data

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DOI:

10.3791/71612

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August 14th, 2026

Priyanka Nalawade¹^,² , Prashant Kumbharkar¹ , Pankaj Agarkar³ , Avanish Kumbharkar⁴ , Sudan Jha⁵

¹School of Engineering, Ajeenkya D Y Patil University, ²Department of Computer Engineering (Software Engineering), MIT Academy of Engineering, ³Ajeenkya D Y Patil SOE Lohegaon, ⁴MS Scholar, The University of Sydney, ⁵Department of Computer Science and Engineering, Kathmandu University

Summary

This method presents a comprehensive explainable artificial intelligence (XAI) based framework to enable secure healthcare cloud data migration, leveraging a synthetic healthcare dataset within a controlled cloud environment. The result is a prototype that combines zero-trust security, time-based access control, and explainable anomaly detection to support migration transparency and security.

Abstract

In healthcare systems, more and more cloud data migration is being done, but this also changes the times when data transfer is probably the biggest risk in terms of security. This paper describes a reproducible protocol for explainable artificial intelligence (XAI)-based secure cloud data migration using a synthetic healthcare dataset and a controlled cloud environment. The developed framework merges zero-trust architecture, temporal least privilege, encrypted communication, centralized monitoring, and explainable anomaly detection to have a more secure, transparent, and auditable migration. The tests use a 10 GB dataset of synthetic electronic health records, comprising approximately 20 million records across 28 relational tables. The migration process was carried out on Amazon web services (AWS) using PostgreSQL databases and private virtual networks. For anomaly detection, Isolation Forest was utilized, and Shapley additive explanations (SHAP) served for the secure event interpretation. The framework was evaluated on ten separate migration attempts using metrics such as credential exposure duration, incident detection time, anomaly-detection accuracy, migration latency, and data integrity. Under the configuration tested, credential exposure was reduced from 24 h to 1 h (a 95.8% reduction), the accuracy of anomaly detection was 97.4%, incident detection time was reduced to about 15 min, and 100% data integrity was preserved through checksum validation. However, the stronger security measures resulted in an average migration latency increase of 11%. These results showcase the promise of merging explainable AI with secure cloud migration workflows for managing healthcare data.

Introduction

Cloud computing is now an integral part of healthcare systems worldwide, offering scalable storage, computational resources, and the ability to exchange health records, support decision-making systems, and enable health analytics through the cloud¹^,²^,³. With many healthcare institutions upgrading their information systems, moving to the cloud has become a vital step for them in order to transfer their sensitive health data held in the older on-premises systems to the cloud⁴. Proper migration leads to easier data retrieval, running operations in a more ....