The CEDAR project harnesses artificial intelligence (AI) and Common European Data Spaces to detect fraud and bring accountability to over €2tn in annual EU public spending
Public procurement is the engine room of the European economy, approximately 14% of the EU’s GDP, over €2tn annually. Yet for decades, this vast expenditure has been managed through fragmented, localised systems that often lack transparency, leaving the process vulnerable to inefficiency, fraud, and corruption. The CEDAR project (Common European Data Spaces and Robust AI for Transparent Public Governance), a Horizon Europe initiative running from 2024 to 2026, is designed to revolutionise the full lifecycle of procurement data management. By leveraging advanced AI and the transformative power of Common European Data Spaces (CEDS), CEDAR goes beyond digitising records to build a cross-border intelligence layer that ensures public money is spent accountably and effectively.
From fragmented data to FAIR intelligence
The primary hurdle in public governance has never been a lack of data, but rather its siloed nature. Procurement notices, contract awards, and invoices are often scattered across thousands of regional portals in varying formats, from structured databases to unsearchable PDFs. CEDAR addresses this through a robust data management cycle rooted in the FAIR principles (Findable, Accessible, Interoperable, and Reusable). The project implements a sophisticated DataOps and MLOps pipeline that automates the ingestion, cleaning, and harmonisation of data from diverse sources. Using a focused ontology and a knowledge graph that links tenders, bids, and actors, CEDAR creates a ‘common language’ for procurement, ensuring that a contract award in Italy can be semantically compared to one in Slovenia or Ukraine.
Advanced AI for continuous public integrity checks
At the heart of the CEDAR architecture lies a suite of advanced AI models designed to act as a sentinel. Unlike traditional oversight, which often relies on manual audits of a tiny fraction of contracts, CEDAR’s AI analyses 100% of available data in real time. The system utilises explainable AI (XAI) and graph-based data modelling to detect patterns invisible to the human eye, triggering automated alerts for further investigation by public authorities. This shifts the focus from random sampling to evidence-based risk management, flagging indicators such as single-bidding, purchase splitting, bid rotation, and tailored tenders that favour specific contractors.
The central vision: Common European data spaces
While AI provides the analytical power, the CEDS provides the infrastructure for collective intelligence. CEDAR aims to serve as a bridge to the Public Procurement Data Space (PPDS) and other sectoral spaces, including the European Health Data Space and the Green Deal Data Space. By enabling Member States to share aggregated analytical results rather than raw sensitive data, the project creates a dynamic, evolving defence system: an attempt at fraud in one corner of the EU instantly makes the entire Union more resilient.
Progress towards greater transparency
Currently in its final year, the CEDAR project, a consortium of 30 partners including research institutions, tech leaders, and NGOs, is validating its technologies through high-impact pilots in Italy, Slovenia, and Ukraine, focusing on the health sector and medical supply chains. By the end of 2026, CEDAR aims to have shared novel AI-driven indicators and high-quality procurement datasets, providing a blueprint for the EU’s Digital Decade 2030 objectives.
Conclusion
The CEDAR project represents a fundamental shift in how we view the relationship between the state and data. By moving from reactive oversight to proactive, AI-driven management, the EU is setting a global standard for transparent governance. Through the use of CEDS, CEDAR ensures that public procurement is no longer a collection of isolated transactions, but a unified, intelligent ecosystem, where data becomes a tool for building a more fair, competitive, and trustworthy European future.
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