Real-Time Data Governance and Compliance in Cloud-Native Robotics Systems

This study investigates the frameworks and challenges of real-time data governance and compliance in cloud-native robotics systems, focusing on data integrity, cloud security, regulatory adherence, and cybersecurity risks. Using extensive datasets from the Amazon AWS Open Data Registry, the EU GDPR Enforcement Tracker, and Kaggle’s IoT dataset, the analysis explores cloud-native systems' data accuracy, security, and governance. Data were extracted through a standardized process: performance metrics, including latency and error rates, were gathered from Amazon AWS to assess system efficiency, GDPR violation records were analyzed from the EU Enforcement Tracker to understand compliance trends, and data volume and governance metrics from Kaggle’s IoT dataset were correlated to identify governance challenges. Together, these data sources provide comprehensive insights into how cloud-native systems can be optimized for real-time operations. The study highlights the cloud security benefits and governance advantages inherent to cloud-native frameworks, such as real-time monitoring, automated threat detection, and data encryption, which collectively reduce unauthorized access risks while supporting operational efficiency. Findings indicate high data accuracy (0.51% error rate) and low latency (mean of 48.96 ms) across systems; however, processing time variability (standard deviation of 28.61 ms) signals a need for further optimization in time-sensitive environments. The regression analysis of GDPR violations reveals a substantial penalty increase of €53,789.41 per violation, emphasizing the financial risks of non-compliance. Correlation analysis (r = 0.083 for data volume and governance failures) suggests that external cybersecurity threats have a greater impact on governance than internal metrics, underscoring the importance of adaptive governance frameworks that support both data integrity and regulatory compliance in cloud-native robotics systems.