Critical Security Gaps in IoT Ecosystems: A Quantitative Risk Assessment Framework

The rapid proliferation of Internet of Things (IoT) devices has transformed modern industries, enterprises, and personal environments, enabling unprecedented connectivity and automation. However, this growth has also introduced a complex array of security vulnerabilities that remain insufficiently addressed in both practice and research. Critical gaps such as weak authentication mechanisms, insecure firmware updates, inadequate network segmentation, and poor vulnerability management expose IoT ecosystems to high-impact cyber threats. Unlike traditional IT systems, IoT environments are characterized by heterogeneous devices, resource-constrained architectures, and large-scale deployments, which complicate the identification, quantification, and prioritization of security risks. Existing approaches often emphasize qualitative assessments or focus narrowly on technical vulnerabilities without providing a systematic method to measure and compare risks across diverse IoT infrastructures. This study proposes a quantitative risk assessment framework tailored to IoT ecosystems, designed to bridge the gap between technical vulnerabilities and business-oriented decision-making. The framework integrates key risk parameters—vulnerability prevalence, exploit probability, asset value, and exposure factor—into a structured formula that computes the Annualized Loss Expectancy (ALE) for each identified threat scenario. By combining classical information security models with IoT-specific considerations such as device population, patching lag, and supply chain risks, the framework produces measurable outputs that enable organizations to rank threats by financial impact and cost-effectiveness of mitigation. Furthermore, the model incorporates Bayesian updating and Monte Carlo simulation to address uncertainty, allowing decision-makers to visualize risk distributions and confidence intervals rather than relying on point estimates alone.