Artifact Evaluation (AE) is essential for ensuring the transparency and reliability of research, closing the gap between exploratory work and real-world deployment is particularly important in cybersecurity, particularly in IoT and CPSs, where large-scale, heterogeneous, and privacy-sensitive data meet safety-critical actuation. Yet, manual reproducibility checks are time-consuming and do not scale with growing submission volumes. In this work, we demonstrate that Large Language Models (LLMs) can provide powerful support for AE tasks: (i) text-based reproducibility rating, (ii) autonomous sandboxed execution environment preparation, and (iii) assessment of methodological pitfalls. Our reproducibility-assessment toolkit yields an accuracy of over 72% and autonomously sets up execution environments for 28% of runnable cybersecurity artifacts. Our automated pitfall assessment detects seven prevalent pitfalls with high accuracy ($F_1$ > 92%). Hence, the toolkit significantly reduces reviewer effort and, when integrated into established AE processes, could incentivize authors to submit higher-quality and more reproducible artifacts. IoT, CPS, and cybersecurity conferences and workshops may integrate the toolkit into their peer-review processes to support reviewers' decisions on awarding artifact badges, improving the overall sustainability of the process.

Data ecosystems emerged as a new paradigm to facilitate the automated and massive exchange of data from heterogeneous information sources between different stakeholders. However, the corresponding benefits come with unforeseen risks as sensitive information is potentially exposed, questioning their reliability. Consequently, data security is of utmost importance and, thus, a central requirement for successfully realizing data ecosystems. Academia has recognized this requirement, and current initiatives foster sovereign participation via a federated infrastructure where participants retain local control over what data they offer to whom. However, recent proposals place significant trust in remote infrastructure by implementing organizational security measures such as certification processes before the admission of a participant. At the same time, the data sensitivity incentivizes participants to bypass the organizational security measures to maximize their benefit. This issue significantly weakens security, sovereignty, and trust guarantees and highlights that organizational security measures are insufficient in this context. In this paper, we argue that data ecosystems must be extended with technical means to (re)establish dependable guarantees. We underpin this need with three representative use cases for data ecosystems, which cover personal, economic, and governmental data, and systematically map the lack of dependable guarantees in related work. To this end, we identify three enablers of dependable guarantees, namely trusted remote policy enforcement, verifiable data tracking, and integration of resource-constrained participants. These enablers are critical for securely implementing data ecosystems in data-sensitive contexts.