Abstract
In recent years, well-known cyber breaches have placed growing pressure on organizations to implement proper privacy and data protection standards. Attacks involving the theft of employee and customer personal information have damaged the reputations of well-known brands, resulting in significant financial costs. As a result, governments across the globe are actively examining and strengthening laws to better protect the personal data of its citizens. The General Data Protection Regulation (GDPR) updates European privacy law with an array of provisions that better protect consumers and require organizations to focus on accounting for privacy in their business processes through “privacy-by-design” and “privacy by default” principles. In the US, the National Privacy Research Strategy (NPRS), makes several recommendations that reinforce the need for organizations to better protect data.
In response to these rapid developments in privacy compliance, data flow mapping has emerged as a valuable tool. Data flow mapping depicts the flow of data through a system or process, enumerating specific data elements handled, while identifying the risks at different stages of the data lifecycle.
This Article explains the critical features of a data flow map and discusses how mapping may improve the transparency of the data lifecycle, while recognizing the limitations in building out data flow maps and the difficulties of maintaining updated maps. The Article then explores how data flow mapping may support data collection, transfer, storage, and destruction practices pursuant to various privacy regulations. Finally, a hypothetical case study is presented to show how data flow mapping was used by an organization to stay compliant with privacy rules and to improve the transparency of information flows.
Recommended Citation
Jeremy Berkowitz et al., Data Flow Maps—Increasing Data Processing Transparency and Privacy Compliance in the Enterprise, 73 Wash. & Lee L. Rev. Online 802 (2017), https://scholarlycommons.law.wlu.edu/wlulr-online/vol73/iss2/11