Compliance Management for Federated Data Processing

TL;DR

This paper introduces a framework for compliance-aware federated data processing that integrates policy management, workflow orchestration, and LLM-assisted compliance, facilitating secure collaborative data analysis.

Contribution

It presents a novel framework combining policy-as-code, workflow management, and LLMs to improve compliance handling in federated data processing environments.

Findings

Prototype demonstrates translation of legal requirements into machine-actionable policies.

Framework enables compliance management across organizational boundaries.

Integrates LLMs for assisting compliance in federated workflows.

Abstract

Federated data processing (FDP) offers a promising approach for enabling collaborative analysis of sensitive data without centralizing raw datasets. However, real-world adoption remains limited due to the complexity of managing heterogeneous access policies, regulatory requirements, and long-running workflows across organizational boundaries. In this paper, we present a framework for compliance-aware FDP that integrates policy-as-code, workflow orchestration, and large language model (LLM)-assisted compliance management. Through the implemented prototype, we show how legal and organizational requirements can be collected and translated into machine-actionable policies in FDP networks.