Integrating Data Governance and Security into Data Engineering Lifecycles: A Proactive Approach
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V1I4P106Keywords:
Data Governance, Data Engineering, Policy-As-Code, Data Contracts, Data Quality, RBAC, ABAC, Encryption, Key ManagementAbstract
Organizations increasingly rely on data products that span heterogeneous platforms, yet many still bolt governance and security on after pipelines are built, causing rework, audit gaps, and fragile controls. This paper presents a proactive, lifecycle-based framework that integrates governance and security as code throughout the data engineering value chain: ingestion, storage, transformation, orchestration, serving, and archival. Model data contracts, data ownership, and data sensitivity categorization and security primitives encryption, key management, masking/tokenization, and fine-grained authorization (RBAC/ABAC/RLS), and implement them using CI/CD gates and runtime guardrails. Metadata, provenance, and quality promises turn into first-class data, generating ongoing compliance data and minimizing schema drift and incident blast radius. In a 2020 assessment, more trust is associated with acceptable overhead: better data quality through automated tests, reduced security incidents through standardized policy, and expanded coverage of compliance through platform controls of govern once, enforce many. Operating models (stewardship, RACI) and measurements (quality SLOs, policy decision latency, least-privilege scores) that enable scale sustenance. New avenues AI/ML-assisted classification and policy recommendations and automated compliance checking, which maps machine-readable controls to regulations, place assurance as an artifact of standard engineering processes. When organizations move governance and security left and consider them as constraints of design, they can provide resilient, explainable, and reusable data products that can stay in step with changing business and regulatory requirements
References
1. Reid, R., Fraser-King, G., & Schwaderer, W. D. (2007). Data lifecycles: managing data for strategic advantage. John Wiley & Sons.
2. Huff, E., & Lee, J. (2020, July). Data as a strategic asset: Improving results through a systematic data governance framework. In SPE Latin America and Caribbean Petroleum Engineering Conference (p. D031S013R001). SPE.
3. Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148-152.
4. Tilley, S. R. (2000). The canonical activities of reverse engineering. Annals of Software Engineering, 9(1), 249-271.
5. Andersen, J. L., & Merkle, D. (2020). A generic framework for engineering graph canonization algorithms.
6. Raj, A., Bosch, J., Olsson, H. H., & Wang, T. J. (2020, August). Modelling data pipelines. In 2020 46th Euromicro conference on software engineering and advanced applications (SEAA) (pp. 13-20). IEEE.
7. Moyón, F., Soares, R., Pinto-Albuquerque, M., Mendez, D., & Beckers, K. (2020, November). Integration of security standards in devops pipelines: An industry case study. In International Conference on Product-Focused Software Process Improvement (pp. 434-452). Cham: Springer International Publishing.
8. Fielder, A., Li, T., & Hankin, C. (2016). Defense-in-depth vs. critical component defense for industrial control systems.
9. Srinivasan, V. (2011). An integration framework for product lifecycle management. Computer-aided design, 43(5), 464-478.
10. Dimyadi, J., & Amor, R. (2017, July). Automating conventional compliance audit processes. In IFIP International Conference on Product Lifecycle Management (pp. 324-334). Cham: Springer International Publishing.
11. Wang, K., Zipperle, M., Becherer, M., Gottwalt, F., & Zhang, Y. (2020). An AI-based automated continuous compliance awareness framework (CoCAF) for procurement auditing. Big Data and Cognitive Computing, 4(3), 23.
12. Rahul, K., & Banyal, R. K. (2020). Data life cycle management in big data analytics. Procedia Computer Science, 173, 364-371.
13. Daneshpour, N., & Barfourosh, A. A. (2011, June). Data engineering approach to efficient data warehouse: Life cycle development revisited. In 2011 CSI international symposium on computer science and software engineering (CSSE) (pp. 109-120). IEEE.
14. Norman, E. S., Dunn, G., Bakker, K., Allen, D. M., & Cavalcanti de Albuquerque, R. (2013). Water security assessment: integrating governance and freshwater indicators. Water Resources Management, 27(2), 535-551.
15. Rezgui, Y., Beach, T., & Rana, O. (2013). A governance approach for BIM management across lifecycle and supply chains using mixed-modes of information delivery. Journal of civil engineering and management, 19(2), 239-258.
16. Pahl-Wostl, C. (2019). Governance of the water-energy-food security nexus: A multi-level coordination challenge. Environmental Science & Policy, 92, 356-367.
17. Moulos, V., Chatzikyriakos, G., Kassouras, V., Doulamis, A., Doulamis, N., Leventakis, G., ... & Gatzioura, A. (2018). A robust information life cycle management framework for securing and governing critical infrastructure systems. Inventions, 3(4), 71.
18. Campbell, L., & Majors, C. (2017). Database reliability engineering: designing and operating resilient database systems. " O'Reilly Media, Inc.".
19. Haider, W., & Haider, A. (2013, July). Governance structures for engineering and infrastructure asset management. In 2013 Proceedings of PICMET'13: Technology Management in the IT-Driven Services (PICMET) (pp. 1229-1238). IEEE.
20. Mohseni, S., Hassan, R., Patel, A., & Razali, R. (2010, April). Comparative review study of reactive and proactive routing protocols in MANETs. In 4th IEEE International Conference on Digital ecosystems and technologies (pp. 304-309). IEEE.
