Hawk: DevOps-driven Transparency and Accountability in Cloud Native Systems

TL;DR

Hawk introduces DevOps-tailored methods and tools to ensure continuous, accurate transparency and accountability in cloud-native systems, aiding compliance with privacy regulations like GDPR and CCPA.

Contribution

The paper presents novel approaches and proof-of-concept implementations for maintaining up-to-date transparency information during DevOps phases in cloud-native environments.

Findings

Effective integration of transparency components across DevOps phases

Reasonable overheads demonstrated in experimental evaluation

Supports compliance with privacy regulations in agile systems

Abstract

Transparency is one of the most important principles of modern privacy regulations, such as the GDPR or CCPA. To be compliant with such regulatory frameworks, data controllers must provide data subjects with precise information about the collection, processing, storage, and transfer of personal data. To do so, respective facts and details must be compiled and always kept up to date. In traditional, rather static system environments, this inventory (including details such as the purposes of processing or the storage duration for each system component) could be done manually. In current circumstances of agile, DevOps-driven, and cloud-native information systems engineering, however, such manual practices do not suit anymore, making it increasingly hard for data controllers to achieve regulatory compliance. To allow for proper collection and maintenance of always up-to-date transparency information smoothly integrating into DevOps practices, we herein propose a set of novel approaches explicitly tailored to specific phases of the DevOps lifecycle most relevant in matters of privacy-related transparency and accountability at runtime: Release, Operation, and Monitoring. For each of these phases, we examine the specific challenges arising in determining the details of personal data processing, develop a distinct approach and provide respective proof of concept implementations that can easily be applied in cloud native systems. We also demonstrate how these components can be integrated with each other to establish transparency information comprising design- and runtime-elements. Furthermore, our experimental evaluation indicates reasonable overheads. On this basis, data controllers can fulfill their regulatory transparency obligations in line with actual engineering practices.