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Malicious attackers frequently breach information systems by exploiting disclosed software vulnerabilities. Knowledge of these vulnerabilities over time is essential to decide the use of software products by organisations. The purpose of this paper is to propose a novel G-RAM framework for business organisations to assess and mitigate risks arising out of software vulnerabilities.

The G-RAM risk assessment module uses GARCH to model vulnerability growth. Using 16-year data across 1999-2016 from the National Vulnerability Database, the authors estimate the model parameters and validate the prediction accuracy. Next, the G-RAM risk mitigation module designs optimal software portfolio using Markowitz’s mean-variance optimisation for a given IT budget and preference.

Based on an empirical analysis, this study establishes that vulnerability follows a non-linear, time-dependent, heteroskedastic growth pattern. Further, efficient software combinations are proposed that optimise correlated risk. The study also reports the empirical evidence of a shift in efficient frontier of software configurations with time.

Existing assumption of independent and identically distributed residuals after vulnerability function fitting is incorrect. This study applies GARCH technique to measure volatility clustering and mean reversal. The risk (or volatility) represented by the instantaneous variance is dependent on the immediately previous one, as well as on the unconditional variance of the entire vulnerability growth process.

The volatility-based estimation of vulnerability growth is a risk assessment mechanism. Next, the portfolio analysis acts as a risk mitigation activity. Results from this study can decide patch management cycle needed for each software – individual or group patching. G-RAM also ranks them into a 2×2 risk-return matrix to ensure that the correlated risk is diversified. Finally the paper helps the business firms to decide what to purchase and what to avoid.

Contrary to the existing techniques which either analyse with statistical distributions or linear econometric methods, this study establishes that vulnerability growth follows a non-linear, time-dependent, heteroskedastic pattern. The paper also links software risk assessment to IT governance and strategic business objectives. To the authors’ knowledge, this is the first study in IT security to examine and forecast volatility, and further design risk-optimal software portfolios.