Modelling Inherent Risk of Data Intensive Technologies: Quantitatively-differentiated Risk Management Framework Proposal - Grigorescu, Petre-Cornel; Ciurea, Iulia-Cristina (Journal of Telecommunications and the Digital Economy, 2025)
This study introduces a systematic methodology for risk management in data-intensive systems inside regulated environments, with a special emphasis on European Union scenarios. The framework tackles the distinct issues of reconciling regulatory compliance with the necessity for technical innovation. It delineates a risk trajectory throughout multiple phases of the data pipeline: collection, intake, processing, modelling, and application. Each stage corresponds to certain risk controls, ranging from fundamental validations at lower risk tiers to stringent security and accountability protocols for elevated risks. Organisations can mitigate any negative effects and successfully utilise data-driven insights by implementing appropriate controls at each phase. The suggested approach incorporates a quantitative risk formula that considers data volume, parameter complexity, and sensitive data items to yield a comprehensive risk score. Risk levels are assigned through Monte Carlo simulations, ensuring probabilistic accuracy in risk assessment. To enhance applicability, the framework defines risk thresholds and proposes differentiated controls, enabling organisations to simulate risk scenarios before implementation. This flexible framework seeks to promote the secure and responsible development of data-intensive applications, allowing European companies to enhance their competitiveness globally while upholding ethical and legal standards, such as the EU AI Act, or the EU Digital Services Act.