Methodology for Cost-Benefit Analysis in Selecting Economic Information Protection Systems for Distributed Edge/Cloud Environments

Abstract

Purpose: Distributed Edge and Cloud architectures have transformed economic data processing, yet the selection of protection systems for these environments often relies on intuition rather than economic rationality. This study aims to propose a structured, probabilistic Cost-Benefit Analysis (CBA) methodology tailored for selecting economic information protection systems in hybrid Edge/Cloud environments.

Design/Methodology/Approach: Moving beyond traditional deterministic models, this paper synthesizes information security economics (building on the Gordon-Loeb principles) with quantitative risk assessment. The proposed four-dimensional framework evaluates Candidate Protection Systems using a Monte Carlo simulation approach (N=10,000 iterations). It incorporates Beta-PERT distributions to account for parametric uncertainty in threat exposure, while explicitly pricing Edge/Cloud specific structural constraints such as latency-security trade-offs and multi-vendor governance fragmentation. Baseline simulation parameters were derived from the IBM 2024 Cost of a Data Breach Report for the financial sector.

Findings: The simulation results demonstrate that deterministic CBA methodologies systematically miscalculate the viability of protection systems by ignoring risk variance. When regulatory cost avoidance and latency penalties are quantified stochastically, distributed hybrid protection solutions (System C) demonstrate superior risk-adjusted Net Protection Value (NPV-P) at the 95th percentile, despite higher baseline implementation costs, compared to centralized or purely cloud-native alternatives.

Originality/Value: This study bridges a critical gap in information security economics by shifting the selection paradigm from static IT capital budgeting to stochastic financial risk modeling, offering a replicable, algorithm-driven tool for organizations managing sensitive economic data across decentralized infrastructures.