Risk Quant Calculator
Introducing a quantitative risk calculator for cybersecurity risk assessment
Risk Quant Calculator
Background
I have previously spoken about working on quantitative risk analysis as part of my university capstone project. I was inspired to create a quantitative risk analysis methodology that would help address commonly faced challenges in the field of cybersecurity risk assessment, and implemented as a Python program that can be easily used by anyone. It is designed to be open-source and free to use, with the goal of making it accessible to a wider audience. I am excited to share risk_quant_calculator with the community and I hope it will be a valuable resource for those looking to improve their risk assessment processes.
Implementation
The project is implemented in Python, focusing mainly on the calculation of Return on Security Investment (ROSI) as the main metric for identifying benefits of security investments. Calculating ROSI is a common practice in the field of cybersecurity risk assessment, and it is important to understand how to calculate it accurately. ROSI is calculated using the formula:
\[ROSI = \frac{(ALE \times ControlEffectiveness) - CostOfSafetyMeasure}{CostOfSafetyMeasure} \times 100\]Where:
- ALE = Annual Loss Expectancy calculated by AV (Asset Value) multiplied by EF (Exposure Factor) multiplied by ARO (Annual Rate of Occurrence)
- ControlEffectiveness = Percentage of risk reduction achieved by the security measure
- CostOfSafetyMeasure = Cost of the security measure being implemented
- ROSI = Return on Security Investment expressed as a percentage
The Challenge of Uncertainty
It is not always the case that we have all the values ready to input to calculate ROSI. In many cases, we may have to estimate the values based on our best guess or experience, or at best, know the range of values that we can expect. This is where this project comes in, providing a way to calculate uncertain values through providing an upper and lower bound for such values.
Features
Scenarios
The risk_quant_calculator provides targeted risk analysis for the following scenarios:
Simple Before/After Financial Impact Analysis: Determine the effectiveness of a security measure, given all values are certain, using simulations
Optimal Security Control Sequencing Given Uncertainty: Given n security controls implemented at 1 control a year, determine the optimal sequence of controls to implement, given uncertainty in the values
Vendor Selection Given Uncertainty: Given n vendors to choose from, determine the optimal vendor to select based on highest ROSI and highest control effectiveness, given uncertainty in the values
Simulation Mechanisms
The risk_quant_calculator uses different simulation mechanisms to calculate values and ensure accuracy. The following simulation mechanisms are explained in simple terms:
Monte Carlo Simulation: Think of this as playing thousands of “what-if” games. If you’re unsure about something (like how often a system might be attacked), you try many random possibilities and see what typically happens. For example, if you run 1,000 simulations and 300 show significant losses, you might conclude there’s a 30% risk of a bad outcome.
Markov Chain Monte Carlo: Imagine exploring a huge maze efficiently. Instead of randomly teleporting around, you start in one spot and make logical steps based on what you can see nearby. Over time, you naturally spend more time in the important areas. MCMC uses this approach to explore complex risk scenarios, focusing computation where it matters most.
Randomized Quasi-Monte Carlo: If you’re testing soil samples in a field, truly random sampling might accidentally cluster too many tests in one area while missing others. RQMC is like using a strategic grid pattern instead - it ensures more even coverage across all possibilities, giving more accurate results with fewer calculations.
Sensitivity Analysis
In the latter two scenarios, sensitivity analysis is also performed to determine the sensitivity of the results to the input values. This is important because it helps us understand how varying the input values can affect the output results.
For example, if we become more certain about the Annual Rate of Occurrence (ARO) value, we can see how it contributes more or less to the overall ROSI result. This is important for decision-making, as it helps us understand which input values are most critical to the output results.
Output Formats
I will not repeat the example outputs here, as they are already documented in the README.md file. The output is generated in a JSON or PNG format, depending your choice of output.
The JSON output provides a detailed breakdown of the calculations, including the values used, the results of the simulations, and any other relevant information.
The PNG output provides a visual representation of the results through graphs like bar charts, line charts, heatmaps, and more. This makes it easier to understand the results at a glance.
Future Plans
While the project is already more or less complete for now, I do plan to make it more user-friendly and refactor the code to make it more readable, if time allows. More scenarios and features may be added in the future, although I cannot promise anything at this time.
Conclusion
The Risk Quant Calculator represents my vision of a tool that can help organizations make data-driven decisions about their cybersecurity investments. By providing a quantitative risk analysis methodology that is easy to use and understand, I hope to empower organizations to take control of their cybersecurity risk management processes. The open-source nature of this project encourages its use and allows others to adapt it to their needs. I encourage you to explore the project, use it for your own needs, and maybe even fork it to customize it to your own requirements.
References
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