Reengineering Digital Trust In Cloud-Native Data Warehouses: A Security-Driven And Analytics-Enabled Framework

Abstract

The accelerating migration of organizational data infrastructures toward cloud-native data warehousing platforms has profoundly transformed how enterprises generate value, manage risk, and establish digital trust with stakeholders. At the heart of this transformation lies an increasingly complex intersection between distributed systems architectures, cybersecurity governance, intelligent analytics, and socio-technical trust mechanisms. While early generations of database systems were designed primarily for performance and transactional reliability, modern cloud data warehouses such as Amazon Redshift embody a fundamentally different epistemology: they are elastic, service-oriented, algorithmically optimized, and deeply embedded in global networks of data exchange. These characteristics, although enabling unprecedented analytical power, also introduce novel vectors of vulnerability, governance ambiguity, and ethical tension. Drawing upon Worlikar, Patel, and Challa’s detailed exposition of Redshift’s architectural and operational paradigms, this article positions contemporary cloud data warehousing as a core substrate for digital trust in the data-driven economy (Worlikar et al., 2025).

This study integrates classical and contemporary scholarship on network security, authentication, encryption, and distributed systems governance with emerging literature on artificial intelligence, audit automation, and digital trust frameworks. By synthesizing these traditionally siloed domains, the paper argues that trust in cloud data warehouses is not merely a technical artifact but a multi-layered institutional and computational construct shaped by architecture, policy, algorithmic oversight, and user perception. Historical security models such as firewalls, Kerberos authentication, and public-key cryptography are reinterpreted in light of cloud-native abstractions, while modern analytics platforms are shown to depend on machine learning-driven monitoring and governance to maintain legitimacy and reliability (Bellovin & Cheswick, 1997; Clifford et al., 1998; Adelakun et al., 2024).

Methodologically, the research adopts a critical-analytical synthesis of the provided literature, treating each reference as a conceptual node within a larger theoretical network. Rather than empirically measuring breach frequencies or algorithmic accuracy, the study interrogates how different scholarly traditions conceptualize security, risk, trust, and control in data-intensive environments. The results reveal that cloud data warehouses function simultaneously as technical infrastructures and symbolic institutions of trust, where breaches, audit failures, or ethical lapses reverberate far beyond immediate financial losses. The discussion further demonstrates that digital trust is increasingly mediated by algorithmic governance, compliance automation, and AI-driven surveillance, raising new questions about accountability, transparency, and power asymmetries between platform providers and users.

Ultimately, this article contributes a unified theoretical framework for understanding security and trust in cloud-native data warehousing. It contends that platforms like Amazon Redshift represent not only technological evolutions but also socio-economic reconfigurations of how organizations claim credibility, legitimacy, and reliability in the digital age. By bridging classic security theory with contemporary analytics and governance research, the study offers a foundation for future investigations into resilient, ethical, and trustworthy data infrastructures.