The overall goal of our project is to achieve trust in a data-driven food system by implementing Digital Responsibility Goals for the food sector. This will enable new levels of innovation for example in food safety, sustainability, personalized nutrtion, reduction of food waste and fair conditions throughout the entire food chain. The programme works on a clear strategic roadmap (a new virtual food system), a set technological enablers, demonstration of solutions, a structured funding programme with open calls, and measures to guide and support the food ecosystem of third party beneficiaries, citizens, stakeholders. As a consortium, we maintain the perspective that technology is not a means to an end, but acts merely as an empowering enabler, providing the means to achieve a wide variety of innovative and valuable use cases. Use cases that promise to serve a broader audience, provided that adequate access also is considered as a prerequisite. Currently however, technology is primarily developed from the perspective and needs of corporations and / or authorities- a limitation that risks perpetuating or further exacerbating the above-mentioned lack of trust within the markets that they serve. With a more diverse and human-centric driven perspective we believe the new use cases that will emerge and the technology development required to realise them will contribute to a more sustainable ecosystem that is “trustworthy by default”. To truly design for trust, the entire chain of activities and underlying assumptions towards developing technology has to be based on fundamental values like responsibility, privacy and user control - especially when dealing with valuable and sensitive food data. The starting point of all assumptions needs to be the user and their values - not a business model or (legitimate) state interests.

A free, democratic and open EU provides endless opportunities for its people. However, growth is not without risk, especially in cyberspace, in the ubiquity of connected devices and rapid technological change. Criminality is also adapting, seeking opportunity and taking on new forms. CC-DRIVER will use a multidisciplinary approach from the domains of psychology, criminology, anthropology, neurobiology and cyberpsychology to investigate, identify, understand and explain drivers of new forms of criminality. We will focus on human factors that determine criminal behaviours such as cyber juvenile delinquency and adolescent hacking. Scientific investigation of drivers into cybercrime, impact of online disinhibition and the effect of youth decision-making processes will inform our evidence-based intervention, mitigation and deterrence strategies. Our measures will be designed to educate regarding criminality and to divert youth from crime. Our consortium will investigate “cybercrime-as-a-service”, its modalities, purveyors and trends so that Member States, stakeholders and citizens have a shared view of the dimensions of cybercriminality, its impact on our society and economy and what we, collectively and individually, can do to overcome them. We will produce a youth self-assessment online metric tool designed to help understand cybercriminal behaviour and to prompt positive pathways. We will also develop a self-assessment questionnaire so that SMEs, CSOs and other stakeholders can assess their vulnerability to cybercrime attacks. For LEAs, we will produce tools to gather evidence and investigate and mitigate cybercrime operations. We will produce policy templates for combatting online cybercriminality. We will deliver opportunities for EU LEAs to exchange knowledge and experiences with a view to fostering common European approaches and strengthening the European Security Union as an area of freedom, justice, security and, importantly, opportunity.

SafeHorizon offers computer law enforcement agencies (LEAs), emergency response teams (CERTs), and computer security incident response teams (CSIRTs) a toolbox with underdevelopment and improved open-source solutions that will be simple to plug-and-play, maintain, and adapt. SaferHorizon aims to disrupt CaaS by harnessing intelligence collected from internet-facing services (the clear, deep, and dark web), public dumps and leaks (from ransomware groups and hacking forums and channels), as well as datasets from LEAs and security providers, and using machine learning (ML) to identify correlations and extract actionable evidence that can be used in court. Beyond crawling for information, SafeHorizon will actively search for possible leaks of information on illegal services, forums, and software that would allow LEAs to track down users, developers, and operators. SafeHorizon will deliver i) a toolkit that is broken down into individual and interoperable easy-to-use tools (in the form of Docker containers), ii) a monitoring platform that integrates the output of these tools, iii) a correlation engine to find the connections among diverse and big datasets, and iv) datasets and notebooks that will be shared with all EU LEAs and vetted organisations and companies to enable the easy uptake and processing of data to deliver tangible results. Moreover, SafeHorizon aims to give cybercriminals a taste of their own medicine by, e.g., spreading disinformation on hacking, carding, forums and channels to dismantle their rings of trust.

ELOQUENCE is focused on the research and development of innovative technologies for collaborative voice/chat bots. Voice assistant-powered dialogue engines have previously been deployed in a number of commercial and governmental technological pipelines, with a diverse level of complexity. In our concept, such a complexity can be understood as a problem of analysing unstructured dialogues. ELOQUENCE’s key objective is to better comprehend those unstructured dialogues and translate them into explainable, safe, knowledge-grounded, trustworthy and bias-controlled language models. We envision to develop a technology capable of learning by its own, by adapting from a very data-limited corpora to efficiently support most of the EU languages; from a sustainable computational framework to efficient and green-power architectures and, in essence, that may serve as a guidance for all European citizens whilst being respectful and showing the best of our European values, specifically supporting safety-critical applications by involving humans-in-the-loop. Overall, ELOQUENCE’s project considers building on top and to improve of prior achievements in the domain of conversational agents, e.g. recently launched and public-domain Large Language Models (LLMs), such as chatGPT (e.g., more recent versions), or LaMDa most of them developed in non-EU countries. While including key industrial enterprises from Europe (i.e., Omilia, Telefonica, Synelixis), ELOQUENCE will validate the developed technology through (i) safety-critical scenarios with human-in-the-loop for security-critical applications (i.e., emergency services in call centres) and (ii) smart home assistants via information retrieval and fact-checking against an online knowledge base for lesser risky autonomous systems (i.e., home-assistants). ELOQUENCE will target the R&D of these novel conversational AI technologies in multilingual and multimodal environments and demonstrated in several pilots.

The increasing interconnection of technology in healthcare between devices at the physical and cyber levels has transformed these infrastructures into large Health Care Information Infrastructures. Such HCIIs are considered critical and sensitive infrastructures due to their importance for people’s well-being and safety. On the other hand, the evolving digital interconnectivity has also changed the threat landscape, producing a wide range of security and privacy challenges and increasing the danger of potential cybersecurity attacks. The integrated nature introduces new potential entry points for cybersecurity risks. Thus, there is an urgent, pressing need for the Health operators to protect their HCIIs. Efficient situational awareness, incident handling and risk assessment is an important step to acquiring a thorough and common understanding of cyber-attack situations, and is necessary to timely reveal security events and data breaches occurring into HCIIs. Consequently, analysis of incident information is crucial in attempting to detect the presence of a threat, within HCIIs, that has already been detected in other interdependent systems within the same ecosystem. AI4HEALTHSEC proposes a state of the art solution that improves the detection and analysis of cyber-attacks and threats on HCIIs, and increases the knowledge on the current cyber security and privacy risks. Additionally, AI4HEALTHSEC builds risk awareness, within the digital Healthcare ecosystem and among the involved Health operators, to enhance their insight into their Healthcare ICT infrastructures and provides them with capability to react in case of security and privacy breaches. Last but not least AI4HEALTHSEC fosters the exchange of reliable and trusted incident-related information, among ICT systems and entities composing the HCIIs without revealing sensitive corporate details