IAMI aims to revolutionize entity identification and resolution in security, intelligence, and investigation contexts. It addresses the challenges and limitations faced by current identification techniques and offers a visionary solution through advanced AI-powered software. The project's long-term vision is to propel EU LEAs and security/intelligence organizations into a new era of intelligence, investigation, and forensic capabilities. IAMI's core innovation is the development of an AI-powered software system that can seamlessly integrate with existing case management systems and analytic tools. At the heart of IAMI is the concept of the 'Identity-Attributes-Matrix (IAM)', a 3D construct that encompasses a broad spectrum of multi-modal identity-related attributes, including biometric data, non-biometric identity-related data, and analytic results. These attributes are used to facilitate large-scale entity identification/resolution, including a broader range of entity types, simultaneous and rapid multi-entity identification/resolution, systematic and continuous analysis of flux of probe attributes, confidence in noisy/corrupted probe data, reduced false positive rates, access to contact attributes, and the ability to classify fake identities and bots/avatars. Furthermore, IAMI sets the groundwork for effective collaboration and data sharing across European agencies, including international organizations like EUROPOL and INTERPOL through the establishment of a new EU-Wide IAMI enrolled data collection repository for terrorist threat assessment and awareness which enriches their capabilities and assets. The realization of IAMI's vision will be achieved through the demonstration of the developed IAMI solution. It will be communicated and disseminated among EU LEAs and security/intelligence agencies through project activities, including deliverables, training curricula, workshops, and pilots. IAMI represents a significant step forward in EU fight against terrorism.

Across Europe society is changing due to demographic, technological and economic developments. Communities are getting more diverse, both in real life and online. This challenges Law Enforcement Agencies (LEAs) to engage with, and to reassure, communities about safety and security matters in a trustful way. The changing status quo is significantly shaping perceptions of policing, requiring adaptive strategies. To address these challenges, the KOBAN project will create an innovative, research and practice-based Community Policing (CP) initiative, adopting an evidenced based approach.It will focus on co-creation and collaborative security between LEA and non-LEA community members, leading to mutual trust and respecting each other’s contributions. This results in a greater sense of security, whilst contributing to an overall increase in the efficiency and effectiveness of securing all communities including those who are often considered hard to reach and engage. KOBAN’S Capability Model will identify proven methodologies, models, technologies, tools and best practices, and use them as a foundation for future proactive, co-active and reactive CP capabilities. This knowledge will help to build the AI Assistant as well as the App Factory for the development of further CP tools, methods and solutions tailored to the needs of stakeholders and adhering to social, legal, cultural, ethical and gender equality standards. These will be tested and validated in six pilot projects throughout Europe. KOBAN will support capacity building, embedding knowledge, skills and capabilities within organisations and communities leading to bespoke training for both police personnel, municipalities and citizens. In short, KOBAN will lead a cultural and organisational transformation that address current fundamental barriers to effective community policing. With its Capability Driven Approach, KOBAN is setting the stage for future-proof community policing, both online and in the real world.

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.

POLIIICE vision is to advance European LEAs to a novel lawful-interception (LI), investigation and intelligence era in which they will be able to effectively prevent, detect and investigate crime and terrorism amid the new age of communication (5G&Beyond, end-to-end encrypted communication and Quantum based encryption). These new age technologies turn legacy LI solutions to totally in-effective and therefore put significant risk on Europe’s fight against crime and terrorism. POLIIICE will offer, research, validate and demonstrate array of innovative LI measures at cloud & network level as well as at edge device level that together will enable LEAs to efficiently overcome the new age challenges and enable high throughput of its LI. In addition, POLIIICE will research and model QUDDaaS (Quantum unlock, detection and decryption as a service) as an envisaged central service, potentially outsourced at pan EU level, which will harness quantum computing for decryption of lawfully intercepted encrypted communication (which is vulnerable to Quantum’s Shor algorithm), for brute force detection of target-user’s credentials/tokens needed to access encrypted cloud-native apps and for Quantum unlock of lawfully seized edge devices. QUDDaaS may also detect and classify LI communications that are resistant to Quantum decryption power and therefore can’t be decrypted. POLIIICE also aims to improve the information exchange and cooperation among European LEAs by proposing and implementing a mechanism and procedure for exchanging pseudo-anonymized suspect identifiers. POLIIICE is designed for ensuring the cost-effectiveness, security and integrity of the new age LI and will provide the legal and ethical framework for each of its measures while strictly complying with privacy preserving and ethics rules of operation. POLIIICE will contribute to the LI standardization and will recommend EU regulation changes for effective adaptation of POLIIICE vision and innovative LI measures.

ARMADILLO aims to equip Law Enforcement Authorities (LEAs) and more particularly, Police Authorities and Forensic Institutes, with a holistic, ground-breaking, easy-to-use, portable toolset that rapidly detects GHB concentrations in urine, saliva and beverages. To realize this, the project will focus on the design and development of in-situ GHB detection systems, integrating advanced optical spectroscopy and electrochemical techniques for precise GHB identification in the different matrices. Both optical and electrochemical detection techniques will be integrated to portable readers capable of quantitative GHB on-site determination. The optical spectroscopy techniques will explore surface-enhanced signal-based approaches (Raman and fluorescence) alongside paper strip methods, while electrochemical detection will combine a specialized fluidics module and NAD/NADH electrodes. In parallel, GHB-specific antibodies will be developed aiming to further enhance its detection selectivity and accuracy. The project will prioritize the enhanced collection and sharing of forensic evidence, emphasizing on forensics' data management and ensuring interoperability and harmonization across systems. By integrating data fusion mechanisms, it will provide reliable, safe, and court-proof forensic evidence, while also leveraging the potential of AI for refined Raman analysis. Concurrently, it will undertake a comprehensive risk assessment to bolster prevention strategies against drug-facilitated violence and assault, all while standardizing user interfaces and employing advanced visualization and reporting techniques. In order to avoid clinical trials and comply with the ethical considerations including the privacy and safety of any involved human subject, ARMADILLO will assess the accuracy, sensitivity and specificity of its sensing technologies using synthetic samples that simulate a wide range of GHB concentrations in urine and saliva samples.