Efficient And Compliant Access To And Use Of Data (IA) (AI, Data And Robotics Partnership)

Project results are expected to contribute to the following outcomes:

• Lead to the development of secure, compliant and adaptive systems that improve the availability, accuracy, privacy and interoperability of data across the Union.
• Deliver advanced, AI-driven compliance technologies and regulatory tools that reduce administrative burdens, promote regulatory efficiency, and facilitate the implementation of the Data Union Strategy, a Single Market for data, Common European Data Spaces, the European Business Wallet, and the Digital Justice Strategy for 2025-2030.
• Enable more agile regulatory processes, foster mutual recognition of compliance efforts across borders and cross-border cooperation, support interoperability between Member States, and enhance transparency and trust. They will position the Union at the forefront of regulatory innovation, while strengthening the functioning, resilience, competitiveness, and digital leadership of the Single Market.
• Enhance the excellence and competitiveness of companies, professionals, and public administrations by providing innovative, automated solutions to navigate and comply with Union rules seamlessly across borders.
• Enhance public services and strengthen the competitiveness and digital sovereignty of the EU by improved availability and use of high-quality real and synthetic data to train AI systems more effectively.

The scope of this topic is to support the deployment of secure, interoperable, and scalable data management systems, ensuring seamless cross-sector data integration, automation of key processes, and compliance with EU frameworks.

The actions should deliver high-quality, well-structured, secure and compliant data, tailored to evolving societal, industrial, research and public sector needs, underpinning key EU strategies, including the Data Union Strategy, the Apply AI Strategy, the Digital Justice Strategy, and the development of Common European Data Spaces, Data Labs and EuroHPC systems (including the AI Factories). The developed methods, technologies and tools should ensure that data is effectively shared between sectors, disciplines, and participating countries, and that the data is reliable, traceable, and fit for purpose.

The proposal should clearly state (in the abstract and in the introduction) which of the following two areas it addresses. A proposal can address both areas, but it should indicate one of them as the main focus of the proposal, as it will be evaluated accordingly under that area:

• Area 1: The actions under this area should support the development and deployment of advanced, AI-driven compliance technologies and solutions that automate data transactions and key regulatory processes, reduce administrative burdens, and facilitate seamless adherence to EU rules. This includes RegTech/GovTech//LegalTech applications such as digital tools offering real-time compliance guidance, automated rule-drafting assistants for policymakers, and multilingual chatbots providing regulatory support to businesses and professionals. Predictive analytics and risk-based approaches should allow tailored compliance pathways, while integration with national systems and the Single Digital Gateway should promote cross-border mutual recognition and application of the Once-Only Principle. Public administrations should be equipped with automated compliance assessment tools, real-time analytics dashboards, and interoperability frameworks to enhance and streamline regulatory oversight and cooperation. The technologies and solutions should contribute to the principles of fairness, accountability and transparency in AI-driven compliance solutions, including traceability and explainability of automated actions.
• The solutions under Area 1 should adhere to open technical standards, ensuring scalability, inclusiveness, and co-development with private and public stakeholders. Robust cybersecurity, trustworthy AI, trust safeguards, security and privacy cryptographic protection, including via post-quantum cryptography, should be embedded, aligning with EU data protection and digital identity frameworks. Artificial intelligence and machine learning models should be harnessed, to the extent possible/reasonable, to enable data-driven feedback loops that support continuous policy learning, allowing regulators to monitor rule implementation, identify unnecessary burdens, and simplify legislation based on real-time evidence. Where appropriate, the actions under this Area should build on and integrate the privacy-enhancing (including anonymization) technologies developed under earlier topics in the Horizon Europe programme.
• Area 2: The actions under this area should focus on the design and deployment of secure, scalable, and adaptive data management systems that automate key data processes, such as data curation, metadata tagging, ontology management and discovery, labelling, annotation, and quality control, developing and adapting appropriate AI methods and tools for these specific tasks. These systems should facilitate seamless integration and sharing of data across sectors and disciplines, ensuring interoperability, data provenance, data privacy and handling secured against emerging quantum threats via post-quantum cryptography, and compliance with applicable EU legal frameworks. Special emphasis is on enhancing data accuracy, representativeness, and relevance, particularly for use cases in industry, public services, citizen engagement, and the development of trustworthy AI applications, as well as the Common European Data Spaces. The development of such high-quality, semantically rich datasets will be essential to unlock the full potential of AI across domains.
• Furthermore, the actions under Area 2 may also support the generation and use of high-quality synthetic data, including spatial synthetic data, to complement real-world datasets while preserving data privacy via advanced, state-of-the-art cryptographic protection. This may include, among others, the use of AI-enabled generative graphics pipelines to automate the creation of large-scale simulated environments and the application of parallelised and/or neuromorphic computing techniques to train AI models and artificial agents efficiently.

The actions under both areas should take into account the work of the Data Spaces Support Centre, particularly the blueprint for common European data spaces, and build synergies with related Union initiatives such as AI Factories, European Blockchain Services infrastructure, and the European Business Wallet, as well as with sector-specific Common European Data Spaces, and EU Digital Identity Wallet large scale pilots. Close collaboration with relevant European Partnerships, stakeholders, including industry, public administrations, and research organisations, will ensure that the systems meet the practical needs of data users while fostering innovation, competitiveness, and digital sovereignty within the Single Market.