Horizon Europe

HORIZON-CL3-2026-02-CS-ECCC

Enhancing the Security, Privacy and Robustness of AI Models and Systems (SecureAI)

The increasing reliance on AI in cybersecurity, critical infrastructure, and decision-making processes raises concerns about the security and robustness of AI systems. As AI systems become more prevalent, they are increasingly targeted by adversarial attacks that manipulate inputs, compromise training data, or introduce hidden vulnerabilities. This topic aims to strengthen the resilience of AI systems and algorithms against various threats and attacks, such as enhancing their resilience against adversarial attacks, backdoor injections, and data poisoning. Proposals should develop real-time anomaly detection, mitigation techniques to defend against adversarial attacks and robust federated learning techniques, in synergies with leading efforts on AI transparency, and in compliance with the AI Act. The topic is expected to:

Develop robust AI models resistant to adversarial attacks. Exploring techniques to harden AI models and systems against adversarial perturbations, such as adversarial training, robust optimisation, and defence mechanisms that enhance the trustworthiness of AI.

Improve detection of manipulated or poisoned training data. Advancing methodologies to identify and mitigate compromised datasets, leveraging techniques such as anomaly detection, provenance tracking, and automated data validation mechanisms.

Address the concept of Private AI by developing mechanisms that enable AI models to be trained, deployed and operated in privacy-preserving environments, particularly for sensitive use cases, as for example for government and enterprise settings. This includes ensuring AI computations and data remain within trusted execution boundaries (e.g. on-premise or regulated cloud environments), and leveraging existing and emerging privacy-enhancing techniques such as federated learning, secure aggregation, computing on encrypted data, quantum-safe homomorphic encryption and secure inference in deep learning to safeguard the protection of personal and other sensitive data throughout the AI lifecycle.

Faculty of Law CU,

Šafárikovo nám. 6,

810 00 Bratislava,

Grants Department

e-mail: granty@flaw.uniba.sk

Tel. + 421 2 9012 2093

Horizon Europe

HORIZON-CL3-2026-01-FCT-02

Open topic on preventing and countering the misuse of emerging technologies for criminal purposes, including issues related to lawful access to data

New and emerging technologies (e.g., new communication technologies, quantum technologies, new biometrics and identification technologies, cloud computing technologies, generative AI etc.) bring many benefits but also pose a number of new challenges for the police and the judiciary. Therefore, there is a strong need to adequately tackle challenges for Police Authorities stemming from all these new and emerging developments as well as to make sure that the lawful access to data keeps track with these evolutions, respecting applicable legislation and fundamental rights such as personal data protection and privacy.

Under the Open topic, proposals are welcome to address new and emerging technologies that are not covered by the other projects of the previous Horizon Europe Calls Fighting Crime and Terrorism, as well as of the current Call Fighting Crime and Terrorism 2026-2027. Proposals should emphasize adaptive methodologies and frameworks that can evolve in response to new threats and challenges, empowering Police Authorities to act effectively while ensuring adherence to legal standards regarding data access. Thus, research activities proposed within this topic should, in a balanced way, 1) develop modern tools, methodologies and training material for police to tackle the problem of misuse for criminal purposes of the new and emerging technologies under consideration, and 2) address issues related to lawful access to data in this context.

In this topic the integration of the gender and intersectional dimension (sex and gender analysis) in research and innovation content is not a mandatory requirement. Coordination among the successful proposals from this topic should be envisaged to avoid duplication and to exploit complementarities as well as opportunities for increased impact.

The proposals funded under this topic that concern issues which are within the mandate of Europol[1] are expected to engage with the Europol Innovation Lab during the lifetime of the project, including validating the outcomes, with the aim of facilitating future uptake of innovations for the law enforcement community. Similarly, if the proposals concern drug-related issues, they are expected to engage with the EU Drugs Agency during the lifetime of the project, including validating the outcomes. For Police Authorities’ training-related aspects, cooperation of successful proposals with CEPOL is expected, provided that the Agency opts out from applying for funding.

Finally, proposals are expected to address all applicable considerations expressed in the Introduction of the Fighting Crime and Terrorism Destination.

Technology Readiness Level - Technology readiness level expected from completed projects

Faculty of Law CU,

Šafárikovo nám. 6,

810 00 Bratislava,

Grants Department

e-mail: granty@flaw.uniba.sk

Tel. + 421 2 9012 2093

Horizon Eurpoe

HORIZON-CL3-2027-01-FCT-01

Online harms detection and investigation tools using a short development cycle model

As the digital landscape continues to evolve, so too do the myriads of online harms that threaten citizens’ security and well-being. To address these challenges, we invite proposals for the development of detection and investigation tools that employ short development cycle models. This approach emphasizes agility and responsiveness, ensuring that tools can quickly adapt to emerging online threats, such as identity theft, disinformation, deepfakes, spoofing, phishing, digital violence, or, e.g., tools for an early detection as well as real-time monitoring and risk assessment that can identify potential fraudulent sales (“online payment fraud”) before they occur.

This topic welcomes innovative ideas focused on creating efficient detection and investigation tools to combat varying forms of online harms, which should be selected at the beginning of every new development cycle, in agreement with all stakeholders involved in the consortia, especially including concrete needs of Police Authorities. The emphasis on short development cycles allows proposals to remain dynamic, responsive to the fast-paced nature of online threats, and capable of addressing both established issues and new challenges as they arise. Proposals should focus on the iterative process of tool development, integrating feedback from Police Authorities to ensure continuous improvement and relevance in combating online harms. Ultimately, the goal is to foster a proactive and effective response to safeguarding online spaces for all users, regardless of their gender identity or expression.

Proposals are expected to provide ideas on how they would engage with the Europol Innovation Lab during the lifetime of the project. Furthermore, if the proposals concern drug-related issues, they are expected to engage with the EU Drugs Agency during the lifetime of the project, including validating the outcomes. For aspects of training of Police Authorities, cooperation of successful proposals with CEPOL is expected, provided that the Agency opts out from applying for funding. To ensure the active involvement of and timely feedback from relevant security practitioners, proposals should plan a mid-term deliverable consisting in the assessment of the project’s mid-term outcomes, performed by the practitioners involved in the project. Finally, proposals are expected to address all applicable considerations expressed in the Introduction of the Fighting Crime and Terrorism Destination. The project should have a minimum estimated duration of 48 months.

Faculty of Law CU,

Šafárikovo nám. 6,

810 00 Bratislava,

Grants Department

e-mail: granty@flaw.uniba.sk

Tel. + 421 2 9012 2093

Horizon Europe

HORIZON-CL3-2027-02-CS-ECCC-01

Artificial Intelligence for Cybersecurity applications

Artificial Intelligence is increasingly utilised in cybersecurity for threat detection, incident response, and adaptive defence mechanisms. However, AI-driven systems themselves are susceptible to adversarial manipulation and bias. This topic aims to advance AI-based cybersecurity applications while ensuring that AI-driven solutions remain resilient, transparent, and compliant with regulatory frameworks such as the AI Act. In this context, the topic explores the role of all types of AI, including generative AI, in cybersecurity applications, including automated threat detection, adaptive cyber defence, and AI-driven cyber threat intelligence. Proposals should develop solutions for trustworthy AI in cybersecurity contexts including addressing adversarial AI risks, in compliance with the provisions of the AI Act. The topic is expected to:

Develop AI-driven solutions and tools for real-time cyber threat detection. Investigating novel machine learning techniques to detect anomalies, malicious activity, and AI-powered cyber threats in real time, improving situational awareness and response times.

Develop adaptive AI systems capable of evolving with dynamic cybersecurity challenges. Exploring AI techniques that continuously learn from new cyber threats, adapting to emerging attack patterns, while maintaining robustness and explainability.

Support the future enhancements of Security Operation Centres/Cyber Hubs. Developing AI-enhanced SOC frameworks that integrate predictive analytics, automation, and threat intelligence to strengthen proactive defence measures.

Faculty of Law CU,

Šafárikovo nám. 6,

810 00 Bratislava,

Grants Department

e-mail: granty@flaw.uniba.sk

Tel. + 421 2 9012 2093

Horizon Europe

HORIZON-CL3-2027-02-CS-ECCC-02

Secure Computing Continuum (IoT, Edge, Cloud, Data spaces)

This topic aims to advance security across the entire computing continuum, spanning IoT devices, edge computing, cloud infrastructures, AI computing environments, and data spaces. Proposals should address critical challenges such as ensuring data integrity in highly distributed and dynamic environments, implementing robust zero-trust architectures to secure interconnected and heterogeneous systems, and enabling comprehensive protection for sensitive data and processes. Privacy should be considered a core element of these approaches, ensuring that data confidentiality is preserved throughout its lifecycle, in compliance with relevant data protection frameworks, such as GDPR. Solutions are expected to deliver tangible and measurable security improvements across all layers of the continuum, prioritizing scalability, interoperability, trustworthiness, security and resilience against emerging threats.

Proposals are expected to address one or more of the following:

- Develop advanced security solutions for edge to cloud. For example, investigating lightweight cryptographic techniques, including in combination with or based on post-quantum cryptography, incorporated in zero-trust architectures, and decentralized security models to ensure end-to-end protection from edge to cloud, while maintaining privacy during data transmission and processing.

- Enhance interoperability of security measures across different computing layers. Exploring security protocols, identity and access management solutions, and cross-domain authentication mechanisms to seamlessly integrate security controls across diverse computing ecosystems, including privacy-preserving protocols that enable the secure and compliant data exchange.

- Develop portable, deployable PQC acceleration solutions through SW/HW secure co-design to secure user data and computing tasks across heterogeneous platforms and applications in all layers of the continuum.

- Improve resilience against distributed cyber threats. Exploring anomaly detection, including AI-driven anomaly detection, intrusion prevention techniques, and automated response mechanisms to counter emerging threats targeting interconnected infrastructures and data spaces, while upholding privacy through techniques such as federated analysis, secure multi-party computation, Fully Homomorphic Encryption or other.

Faculty of Law CU,

Šafárikovo nám. 6,

810 00 Bratislava,

Grants Department

e-mail: granty@flaw.uniba.sk

Tel. + 421 2 9012 2093