An Agentic RAG and Evaluation Framework for Assurance Case Generation: Industrial Use Case for the EU Cyber Resilience Act Compliance
TL;DR
This work introduces an automated framework for generating Assurance Cases using an agentic Retrieval-Augmented Generation grounded in a formal Claim-Argument-Evidence logic, which provides a scalable, interpretable approach for automating mandatory CRA conformity assessments, reducing manual effort while maintaining transparent decision support.
Abstract
Complying with the EU Cyber Resilience Act (CRA) is a resource-intensive challenge for SMEs due to the complexity of cybersecurity conformity assessment. Yet, it is essential for demonstrating regulatory compliance and ensuring product security and resilience. To address this, we introduce an automated framework for generating Assurance Cases (ACs) using an agentic Retrieval-Augmented Generation grounded in a formal Claim-Argument-Evidence logic. By systematically mapping technical documentation requirements, the framework streamlines the generation of certification evidence. We validate our approach on a case study of Catalink's PATROLIoT wildfire monitoring system, where the agentic RAG generated 70 ACs with high grounding density (~4.4 artefacts per AC). The proposed Natural Language Inference (NLI) evaluator achieves 0.88 accuracy, which provides robust evidence grounding and traceability, while expert-validated plausibility (3.06) supports interpretable justifications. For practitioners, this work provides a scalable, interpretable approach for automating mandatory CRA conformity assessments, reducing manual effort while maintaining transparent decision support.