SHASAI targets the HW/SW security and AI-based high risk systems intersection, aiming to enhance the security, resilience, automated testing, and continuous assessment of AI systems. The rising interest in these systems makes them attractive targets for threat actors due to their complexity and valuable data. Ensuring the security of AI systems involves safeguarding AI models, datasets, dependencies, and securing the underlying HW/SW infrastructure. SHASAI takes a holistic approach of AI system security throughout their lifecycle stages. At requirement definition, SHASAI provides an enhanced risk assessment methodology for secure and safe AI. At design, SHASAI will propose secure and safe design patterns at SW and HW level to achieve trustworthy AI systems. During implementation, SHASAI provides tooling for a secure supply chain of the system by analyzing vulnerabilities in SW / HW dependencies, detecting poisoned data and backdoors in pretrained models, scanning for software vulnerabilities, hardening hardware platforms, and safeguarding intellectual property. At evaluation, SHASAI offers a virtual testing platform with automated attack and defense test suites to assess security against AI and infrastructure-specific threats. In operation, AI-enhanced security services continuously monitor the system, detect anomalies, and mitigate attacks using AI firewalls and attestation methods, ensuring availability and integrity. The feasibility of SHASAI methods and tools will be demonstrated in 3 real scenarios: 1. Agrifood industry: Cutting machines. 2. Health: Eye-tracking systems in augmentative and alternative communication. 3. Automotive: Tele-operated last mile delivery vehicle. Their heterogeneity and complementarity maximize the transferability of solutions. SHASAI will contribute to scientific, techno-economic, and societal impacts as it aligns with the CRA, EU AI Act, NIS2 and CSA, sharing and commercializing methods and tools to ensure trustworthy AI components.

The objective of this research activity is to create a reliable computing node that will create a Cognitive Edge under industry standards. This computing node will be the building block of scalable Internet of Things (from Low Computing to High Computing Edge Nodes). The cognitive skill will be given by an internal and external architecture that allows to forecast its internal performance and the state of the surrounding world. Hence, this node will have the capability of learning how to improve its performance against the uncertainty of the environment. As a result of the integration of these cognitive systems into a fractal network, there will be another intrinsic crucial advantage, emergency and adaptability, new functions will flourish through the created space of possibilities of our cognitive Systems. This complex network will transfer all those cognitive advantages to the Edge, a computing paradigm that lay down between the physical world and the cloud.

The aerospace industry faces significant challenges in bringing new aircraft designs to market, as this concerns a complex certification process that relies heavily on expensive and time-consuming physical tests based on a pyramidal framework (from base to top: material, coupon, element, aerostructure). This approach has notable drawbacks, including a lack of insight into how changes at one level impact the overall aerostructure performance and the need to repeat much of the certification process if changes are made at distant levels. To address these challenges, the pAIramid project proposes a revolutionary approach based on high-fidelity virtual tests interconnected across the different levels of the certification pyramid. A digital tool is being created, which works by leveraging data-driven simulation methods and Artificial Intelligence (AI), aiming to optimize the certification process, reducing computational time, and promoting fast decision-making. This AI-driven hybrid pyramid approach breaks down barriers between different testing levels, easing knowledge transfer and faster design iterations. The pAIramid project is completed with several industrial demonstrators, which will help to check the proper performance of the digital tool while proving that it is able to effectively bring in new solutions to the aerostructures’ field. Four different use cases, all of them focused on advancing technologies related to composites’ properties (functionalized thermosets and thermoplastics) and manufacturing processes (one-shot LRI and FDM with continuous fiber reinforcement) are analyzed. All of them will be matured up to TRL4, counting with relevant collaboration of RTOs and industrial partners, which give these technologies the potential to be deployed in the market in the coming years, as well as representing valuable information for the tool learning, which will continue growing thanks to already existing and newly created data, while spreading in the market.