With the rapid advancements in Artificial Intelligence (AI), autonomous agents are increasingly expected to manage complex situations where learning-enabled algorithms are vital. However, the integration of these advanced algorithms poses significant challenges, especially concerning safety and reliability. This research emphasizes the importance of incorporating human-machine collaboration into the systems engineering process to design learning-enabled increasingly autonomous systems (LEIAS). Our proposed LEIAS architecture emphasizes communication representation and pilot preference learning to boost operational safety. Leveraging the Soar cognitive architecture, the system merges symbolic decision logic with numeric decision preferences enhanced through reinforcement learning. A core aspect of this approach is transparency; the LEIAS provides pilots with a comprehensive, interpretable view of the system's state, encompassing detailed evaluations of sensor reliability, including GPS, IMU, and LIDAR data. This multi-sensor assessment is critical for diagnosing discrepancies and maintaining trust. Additionally, the system learns and adapts to pilot preferences, enabling responsive, context-driven decision-making. Autonomy is incrementally escalated based on necessity, ensuring pilots retain control in standard scenarios and receive assistance only when required. Simulation studies conducted in Microsoft's XPlane simulation environment to validate this architecture's efficacy, showcasing its performance in managing sensor anomalies and enhancing human-machine collaboration, ultimately advancing safety in complex operational environments.

Securing resource-constrained Industrial Internet of Things (IIoT) devices requires guarantees of integrity and confidentiality. This paper presents a security framework developed under the CERTIFY project to document and justify trustworthy execution of sensitive software running on resource-constrained devices. Our architecture model assumes the presence of a separation kernel, that employs hardware Memory Protection Units to enforce Memory isolation and mediate communications during boot, provisioning, and run-time. On the other side, the paper does not cover implementation aspects, but leaves them to the choice of the user intending to adopt the proposed solution architecture. To systematically show the trustworthiness of this proposed architecture, we develop an assurance case using Goal Structuring Notation: we map system requirements to architecture-specific security objectives and introduce a novel “attacker action” graphical element to explicitly integrate threat modeling into our arguments. The resulting assurance case provides a structured, auditable, and reusable foundation architecture for the secure implementation of the CERTIFY IIoT framework.