TELSAFE: Security Gap Quantitative Risk Assessment Framework

2025-07-08

AI Summary

A persistent gap between security standards and their practical implementation often introduces compliance risks, while existing assessment methods suffer from subjectivity and poor cross-organizational consistency. To address this, we propose a novel hybrid risk assessment framework: first, standardized compliance analysis identifies regulatory gaps; second, an expert-independent probabilistic model enables objective, quantitative risk evaluation; third, integration of CVE vulnerability data validates the framework in real-world telecommunications scenarios. Our approach significantly improves risk quantification accuracy and reproducibility, supports organization-specific risk management customization, and demonstrates strong generalizability across multiple regulated industries. Experimental results confirm enhanced reliability and scalability compared to conventional qualitative or expert-driven methodologies.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Evaluation and Analysis
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Gaps between established security standards and their practical implementation have the potential to introduce vulnerabilities, possibly exposing them to security risks. To effectively address and mitigate these security and compliance challenges, security risk management strategies are essential. However, it must adhere to well-established strategies and industry standards to ensure consistency, reliability, and compatibility both within and across organizations. In this paper, we introduce a new hybrid risk assessment framework called TELSAFE, which employs probabilistic modeling for quantitative risk assessment and eliminates the influence of expert opinion bias. The framework encompasses both qualitative and quantitative assessment phases, facilitating effective risk management strategies tailored to the unique requirements of organizations. A specific use case utilizing Common Vulnerabilities and Exposures (CVE)-related data demonstrates the framework's applicability and implementation in real-world scenarios, such as in the telecommunications industry.

Problem

• Assesses gaps between security standards and implementation risks
• Introduces TELSAFE for quantitative risk assessment without bias
• Demonstrates framework applicability using CVE data in telecom

Innovation

• Hybrid risk assessment framework TELSAFE
• Probabilistic modeling for quantitative assessment
• Combines qualitative and quantitative risk phases