The interconnection between Information Technologies and Operational Technologies is underway, with many impacts on the related cybersecurity of various application domains. For a long time, we have known that attacks or malfunctions in the cyber world can have critical impacts on the physical world, especially in critical infrastructures. Conversely, intentional perturbations of physical systems, through e.g. attacks on sensor measurements, can have disastrous consequences on digital control mechanisms, and thus on physical processes. In this interconnected cyber-physical world, the advent of Artificial Intelligence (AI) opens the door to various new kinds of attacks, and also offers numerous defence capabilities. In the KINAITICS project, we aim at exploring the new attack opportunities offered by the introduction of AI-based control and perceptive systems, as well as those offered by combination of behavioural understanding of physical systems and cyber-attacks. On the defence side, we aim at offering an innovative spectrum of tools and methodologies, to combine behavioural monitoring and classical cybersecurity tools to protects against these new threats. Importantly, we also target innovative methodologies, which incorporate human factors and their uncertainties in the tools. This last point raises crucial challenges on trustworthy approaches, explanations provided, and how to deal with uncertainties in response decisions. The project will also thoroughly assess the regulation of big data uses and provide guidelines for EU policy actions and cybersecurity experts’ responsible development, thanks to the implication of researchers specialised in legal and ethical aspects of ICT innovation. Our research will strive to counter AI attacks. The seven tools produced during the course of the project, as well as the cyber-defence platform, will significantly improve systems robustness, resilience and response, and will help Europe save 3-4 billion€ yearly by 2030.

Agriculture is being managed more tightly than ever before and is generating more data than ever before, but the potential of a data economy in agriculture remains unexplored. The reasons for this are varied, and include technical interoperability, business relationships between stakeholders, and social acceptability issues around data ownership and market transparency. Individual stakeholders make use of the data they generate at their own particular stage in the agri-food supply chain. However, the sharing of this data with others along the chain and its collective analysis needs more development and demonstration if more efficiencies are to be introduced and further value added to the agri-data economy. While some sharing is taking place on an ad-hoc basis, each new set of potential data sharers must start from scratch and work through the same issues common to all such arrangements. Equally, the lack of data sharing precedents in agriculture inhibits data owners from taking a more exploratory view of the world. Several dimensions must be considered in policy-making if a fully functioning data economy in the agriculture domain is to emerge. Such a multi-disciplinary approach is at the core of the DIVINE consortium, which encompasses technical (agriculture and ICT), markets, and social sciences expertise. It will build an agri-data ecosystem that incorporates existing common agri data spaces while deploying industry-led pilots built on data sharing arrangements, to demonstrate the cost-benefit and added value in sharing agri data. DIVINE will assess its ecosystem at the level of policy impacts, the uptake of digital technologies, and economic and environmental performance. DIVINE will promote its ecosystem and its assessments to technology providers, policy-makers, farm representatives, and various other agri-data stakeholders. It will take the first real concrete steps towards mature data markets in European and global agriculture.

PLIADES advances the SoA dataspaces reference architectures, towards a step change on the use of data as key enabler of technological advances in AI and Robotics. To this end, PLIADES researches into novel, AI-enabled tools to advance full data life cycles integration, both within and between data spaces. Sustainable data creation methods through data compression, filtering and normalization will be developed, to allow efficient and greener storage in a data-oriented future. Data privacy and sovereignty will be further ensured, through standards and decentralized protocols to protect data-producing organizations and citizens. Alongside, data sharing will be revolutionized through novel AI-based brokers and connectors using extended metadata, shaped through the project’s best practices and domain expert’s knowledge. On top of these, active data discovery services through cross domain AI connectors will allow creating linked data spaces, enabling interoperability between previously disconnected entities, while data quality assessment services will facilitate real time data evaluation. Extended synergies with EU initiatives will be established in order to contribute models, strategies and technologies for a Common European Data Space. Our outcomes will be evaluated in six use cases focusing on direct advancements in key AI and Robotics technologies for everyday use, oriented around multiple data spaces; mobility, healthcare, industrial, energy and green deal. Our use cases provide a challenging validation suite involving vast heterogeneous data creation, management and sharing while addressing full data lifecycles in multiple major domains. Through the developed ecosystem, CCAM and ADAS/AD car technologies will be enhanced, HRI for robot operators and healthcare patients will be reshaped, while further advanced, integrated data spaces will be deployed in the healthcare, manufacturing and green deal sectors aiming to reduce carbon footprints and shape a greener future.

The proposed solution aims to deliver a descriptive and predictive data analytics platform and related tools using state-of-the-art machine learning and artificial intelligence methods to prevent, detect, analyse, and combat criminal activities. AIDA will focus on cybercrime and terrorism, by addressing specific challenges related to law enforcement investigation and intelligence. While cybercrime and terrorism pose distinct problems and may rely on different input datasets, the analysis of this data can benefit from the application of the same fundamental technology base framework, endowed with Artificial Intelligence and Deep Learning techniques applied to big data analytics, and extended and tailored with crime- and task- specific additional analytic capabilities and tools. The resulting TRL-7 integrated, modular and flexible AIDA framework will include LE-specific effective, efficient and automated data mining and analytics services to deal with intelligence and investigation workflows, extensive content acquisition, information extraction and fusion, knowledge management and enrichment through novel applications of Big Data processing, machine learning, artificial intelligence, predictive and visual analytics. AIDA system and tools will be made available to LEAs through a secure sandbox environment that aims to raise the technological readiness level of the solutions through their application in operational environment with real data.