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The European Union (EU) Artificial Intelligence (AI) Act introduces stringent requirements for AI systems, posing challenges for organisations seeking compliance. This study explores whether the AI Governance and Assurance (AIGA) framework can provide a structured approach to aligning the DYNAMO platform with these regulations. The Horizon DYNAMO project, funded by the EU, aims to improve cyber resilience by collecting the organisation’s skills data and creating custom and modified training programs. The DYNAMO project integrates AI solutions for threat intelligence and other purposes. The hypothesis is that the AIGA framework offers a robust governance structure that ensures compliance, supports ethical decision-making, and enhances transparency throughout the AI lifecycle. Using desk research, published literature by AIGA’s research team and EU AI Acts regulatory guidelines were analysed to evaluate the AIGA framework. This study focuses on applying AIGA to the governance phase, specifically addressing workflows and structures that embed compliance checkpoints and risk management mechanisms. This approach directly tackles key aspects of the EU AI Act, including risk-based system classification, transparency obligations, and continuous monitoring. Findings indicate that during the AIGA framework development, AIGA’s researchers paid particular attention to the emerging EU AI legislation, which caused it to align well with the regulatory requirements of the EU AI Act. To manage AI development and ensure compliance, the AIGA model offers root-level actions and a practical governance checklist. Implementing these governance tasks in a dynamic platform like DYNAMO requires further refinement and adaptation to each critical sector's specific environmental and stakeholder requirements to produce practical applicability. In conclusion, this study demonstrates that the AIGA framework can provide a strong foundation for regulatory compliance under the EU AI Act. By addressing governance challenges, this approach enables organisations to meet regulatory demands while maintaining ethical AI development and operational excellence and contributing to a future where AI is both innovative and responsible. Future work includes testing these implementations in real-world scenarios to confirm their effectiveness.
Tikanmäki et al. (Wed,) studied this question.