Abstract

Big data analytics (BDA) and artificial intelligence (AI) technologies have become integral to modern decision-making, transforming industries and organisations by enabling the analysis of vast datasets to uncover patterns, forecast trends, and optimise processes. However, the increasing adoption of these technologies has raised significant concerns about data privacy and ethical considerations (Howe & Elenberg, 2020). The European Union’s General Data Protection Regulation (GDPR) was among the first global frameworks to establish legal guidelines for the collection, storage, and processing of personal data. It mandates organisations to conduct data protection impact assessments (DPIAs) to identify and mitigate risks to individuals’ rights and freedoms (EU, 2016). Similarly, the California Consumer Privacy Act in the United States requires organisations to perform privacy impact assessments (PIAs), which serve as proactive tools for evaluating and addressing data processing risks (CA.GOV, 2018). Both DPIAs and PIAs provide a structured approach that enables organisations to systematically identify and manage privacy risks associated with processing personal data. Beyond risk management, these assessments are essential for demonstrating compliance and accountability (Butin & Le Metayer, 2015; Demetzou, 2019). Our research, conducted in multiple stages, explored how BDA operations can adhere to stringent principles and regulations governing the handling of personal data, with a particular emphasis on sensitive data. Specifically, we examined the extent to which the current DPIA framework enables organisations to proactively identify and address vulnerabilities in BDA-driven personal data processing, mitigating risks before they escalate into critical issues (WP29, 2017).

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