This research investigates the development of automatic and semi-automatic methods and tools to support organizations in achieving compliance of their AI-based software systems with regulations such as the AI Act, GDPR, NIS2, CRA, and DORA.

Our goal is to produce a set of interoperable tools and patterns that operationalize compliance-by-design principles, promote transparency, and reduce the compliance burden on organizations deploying AI-enabled solutions.

Selected Publications

Al-Obeidallah, M., Piras, L., Iloanugo, O., Mouratidis, H., Alkubaisy, D. and Dellagiacoma, D.: Goal-modeling privacy-by-design patterns for supporting GDPR compliance. In Proceedings of the 18th International Conference on Software Technologies (ICSOFT), 2023.

Piras, L. et al. “DEFeND DSM: A Data Scope Management Service for Model-Based Privacy by Design GDPR Compliance” in Int. Conf. on Trust, Privacy and Security in Digital Business (TrustBus). Springer, 2020.

Thiha, M., Yetgin, H., Piras, L. and Al-Obeidallah, M.G.: Enhancing Privacy, Censorship Resistance, and User Engagement in a Blockchain-Based Social Network. In 20th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE). 2025.