Abstract

As enterprises increasingly adopt multi-cloud strategies to leverage diverse cloud service providers, the cybersecurity landscape has become more complex and vulnerable to evolving threats. Multi-cloud environments, while offering flexibility, redundancy, and scalability, inherently present amplified security risks due to the heterogeneity of platforms, varied security protocols, and increased attack surfaces. This paper presents a quantitative framework for modeling cybersecurity risks in multi-cloud architectures by integrating probabilistic risk assessment techniques with real-time threat intelligence metrics. The proposed model moves beyond traditional qualitative assessments by introducing a data-driven methodology that incorporates statistical modeling, attack surface quantification, and system-level vulnerability scoring.
Drawing on the foundational principles of threat modeling and Bayesian inference, this framework enables stakeholders to compute the conditional probabilities of breach occurrences based on varying security configurations and provider-specific controls. By simulating adversarial behavior and correlating it with historical incident data, the model dynamically updates risk scores in response to changing infrastructure or attacker profiles. Moreover, the study proposes a federated trust scoring mechanism that accounts for inter-cloud trust relationships, vendor-specific compliance obligations, and systemic propagation of breaches across platforms. This feature is crucial for capturing risk interdependence in federated ecosystems.
A critical contribution of this research is the development of a Cyber Risk Propagation Index (CRPI), which quantifies the extent to which a breach in one cloud domain may cascade across connected services or hybrid configurations. The model is validated using synthetic workloads and simulated attacks on testbed environments modeled after real-world deployment topologies, ensuring generalizability and practical relevance. The findings highlight the need for dynamic, responsive risk modeling tools that reflect the fluid architecture of multi-cloud operations and inform adaptive defense strategies.
This work ultimately provides cybersecurity professionals, risk managers, and enterprise architects with a robust analytic instrument to assess, compare, and mitigate cyber risks across multi-cloud environments in real time. It underscores the urgent need for quantitative rigor in multi-cloud cybersecurity planning, particularly as organizations transition to decentralized digital infrastructures that demand interoperable and predictive security frameworks.