PCI-DSS–Aligned Analytics Pipelines: Tokenization, Vaulting, and PII Minimization for Payment Data
Senior Data Architect, FinTech Domain, USA.
Research Article
World Journal of Advanced Research and Reviews, 2022, 16(01), 1258–1269
Publication history:
Received on 02 September 2022; revised on 24 October 2022; accepted on 28 October 2022
Abstract:
The swift increase in the number of digital payments has increased the necessity of effective security services that might secure delicate payment card data. The compliance with the Payment Card Industry Data Safety Standard (PCI-DSS) provides a systematic methodology to safeguard the data of the cardholders (CHD) and reduce the risk of the data breach. The modern analytics pipelines usually handle large quantities of payment data in order to extract actionable insights; nonetheless, it brings about enormous privacy and compliance pressures. The current paper details an analytics framework, which is aligned to PCI-DSS that focuses on tokenization, vaulting, and minimization of Personally Identifiable Information (PII). The analysis remains useful, whereas tokenization substitutes sensitive card data with its non-sensitive counterparts, which substantially shrink the analysis surface. The creation of sensitive tokens or limited amounts of PII in a controlled environment with minimally sensitive data or information, binding secured to vaulting allows only access within a rigorously regulated setting. The minimization techniques of PII minimize exposure because only the necessary data is collected, and the sensitive data are made anonymous as much as possible. The proposed study introduces an end-to-end analytics pipeline, which combines these approaches and emphasizes operational efficiency, as well as compliance with regulations. This paper determines the performance and compliance advantages of this framework by doing a comparative study with the standard data processing pipelines. The results of the experiments indicate that the compliance risk is reduced significantly and the data security is improved without any weakening of analytical tools. The framework also facilitates auditability, real-time observations and it can be combined with the existing enterprise analytics platforms. With the compliance to the PCI-DSS standards and the use of advanced security measures on data, the offered pipeline has become a scalable, secure, and privacy-conscious service to support the recent payment analytics. The results point to the balance that is very critical between data utility and regulation compliance, and give recommendations to organizations that want to implement effective and secure payment analytics.
Keywords:
PCI-DSS; Tokenization; Vaulting; PII Minimization; Payment Data; Analytics Pipelines; Data Security; Compliance
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