Abstract

This dissertation delves into the pressing issue of maintaining real-time data integrity and provenance. Specifically, it focuses on dynamic financial microservice environments. The proposal is a zero-latency data provenance layer, one that uses both predictive integrity models and blockchain anchors. A thorough analysis of current microservice architectures and integration methods is conducted. This reveals notable shortcomings in how data trustworthiness and traceability are currently handled. The research suggests that predictive models can bolster the reliability of data integrity checks. Furthermore, blockchain anchors offer immutable records, aiding in smooth auditing and verification. This two-pronged strategy not only boosts the speed and precision of data provenance systems but also assures adherence to financial sector regulations. The value of these results isn't confined to finance alone. Indeed, it presents important lessons for healthcare systems too. These systems also heavily rely on sensitive data's integrity and traceability. By illustrating the practicality and effectiveness of this novel framework, the study highlights opportunities for enhancing data management. This can, in turn, markedly improve decision-making in scenarios where precision is of the utmost importance. As a result, the research has wider implications. It could affect the design of secure, efficient, and transparent data infrastructures across different industries, fostering greater trust in digital exchanges and the validity of data-driven choices.