  Converging Zero Trust (ZT) with learning techniques can solve various
operational and security challenges in Distributed Computing Continuum Systems
(DCCS). Implementing centralized ZT architecture is seen as unsuitable for the
computing continuum (e.g., computing entities with limited connectivity and
visibility, etc.). At the same time, implementing decentralized ZT in the
computing continuum requires understanding infrastructure limitations and novel
approaches to enhance resource access management decisions. To overcome such
challenges, we present a novel learning-driven ZT conceptual architecture
designed for DCCS. We aim to enhance ZT architecture service quality by
incorporating lightweight learning strategies such as Representation Learning
(ReL) and distributing ZT components across the computing continuum. The ReL
helps to improve the decision-making process by predicting threats or untrusted
requests. Through an illustrative example, we show how the learning process
detects and blocks the requests, enhances resource access control, and reduces
network and computation overheads. Lastly, we discuss the conceptual
architecture, processes, and provide a research agenda.
