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

20 Pages Posted: 8 Jan 2025

See all articles by Onyinye Obioha Val

Onyinye Obioha Val

University of the District of Columbia

Oluwatosin Selesi-Aina

University of Lagos

Titilayo Modupe Kolade

Federal Government of Nigeria - Ministry of Foreign Affairs, Nigeria

Michael Olayinka Gbadebo

University of the Cumberlands

Omobolaji Olateju

University of Ibadan - Department of Chemistry

Oluwaseun Oladeji Olaniyi

University of the Cumberlands

Date Written: November 12, 2024

Abstract

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 realtime 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.

Keywords: Real-time data governance, cloud-native robotics, GDPR compliance, cybersecurity, data integrity

Suggested Citation

Obioha Val, Onyinye and Selesi-Aina, Oluwatosin and Kolade, Titilayo Modupe and Gbadebo, Michael Olayinka and Olateju, Omobolaji and Olaniyi, Oluwaseun Oladeji, Real-Time Data Governance and Compliance in Cloud-Native Robotics Systems (November 12, 2024). Available at SSRN: https://ssrn.com/abstract=5018252 or http://dx.doi.org/10.2139/ssrn.5018252

Onyinye Obioha Val

University of the District of Columbia ( email )

Oluwatosin Selesi-Aina

University of Lagos ( email )

Titilayo Modupe Kolade

Federal Government of Nigeria - Ministry of Foreign Affairs, Nigeria ( email )

Michael Olayinka Gbadebo

University of the Cumberlands ( email )

Omobolaji Olateju

University of Ibadan - Department of Chemistry ( email )

Oluwaseun Oladeji Olaniyi (Contact Author)

University of the Cumberlands ( email )

6178 College Station Drive
Williamsburg, KY 40769
United States

HOME PAGE: http://www.ucumberlands.edu

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