
This paper presents the Cluster SequoIA’s roadmap, addressing trustworthy AI for security in digital systems, with a focus on cybersecurity, defense, and environmental applications. The work is structured around three pillars: core AI (secure, explainable, and hybrid models; lifelong learning; formal verification), AI for cybersecurity and defense (vulnerability assessment, adversarial robustness, and dynamic security policies), and AI for environment and ocean (modelling complex systems, uncertainty quantification, and heterogeneous data integration). Transversal challenges, such as continuum computing, MLOps, ethical/legal frameworks, and user-centric design, emphasize the need for interdisciplinary collaboration to ensure AI’s responsible and secure deployment in critical domains.
Cybersecurity, Hybrid AI, Adversarial Robustness, Environmental security, Trustworthy AI
Cybersecurity, Hybrid AI, Adversarial Robustness, Environmental security, Trustworthy AI
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
