The Cyber Casino: Managing Risk with Monte Carlo Simulations

Cyber risk presents a pervasive and ever-evolving challenge. As organizations navigate the complex landscape of cyber risk, the need for robust and adaptable risk assessment tools increases.

Excel's random function and data table forecasts are two key quantitative tools that are both accessible and powerful for supporting assessment and management of cyber risk with Monte Carlo simulations.

Together, these tools offer a combination that can help advisors drive informed decisions, optimize security investments, and better protect against potential threats.

Using Excel's Random Function in Cyber Risk Analysis

Microsoft Excel provides many powerful yet easy-to-use features for cyber risk analysis. The random function can be harnessed to model different cyber threat scenarios and generate actionable insights.

Excel's random function, available through formulas like RAND() and RANDBETWEEN(), generates random numbers that can be used to simulate various scenarios. When applied to cyber risk analysis, random numbers can represent different facets of threats, such as likelihood and impact.

Using random numbers to model different cyber threat scenarios:

1. Identify the Variables: Decide which variables need to be randomized according to your risk model, such as a probabilistically determining whether or not multiple cyber risk events occur during a period.

2. Utilize the RAND Function: Employ the RAND() function to generate random numbers for the variables identified. For example =IF(RAND()<[likelihood_probability], 1, 0) could be used to randomly generate a 1 or 0 at the probability rate that was used for a cyber event in a threat model.

3. Create Scenarios: Using the randomly generated 1 or 0 in combination with the potential impact of multiple events could simulate the aggregate impacts of multiple events happening in an assessment period, such as one year.

4. Analyze the Results: Understand the potential combined outcomes and assess the simulated amount of risk associated with different scenarios.

For example, by applying Excel's random function, an organization can model scenarios like the impact of a data breach and a phishing attack happening in the same year. In reviewing the scenario and analyzing the results, the firm might better understand the consequences of multiple loss events happening during the same period.

A simulated scenario using Excel’s random function

(download this template in Excel)

While Excel's random function offers an accessible and flexible way to model cyber risk scenarios, individual probabilities may not fully capture complex correlations between variables—such as the likelihood that one cyber event increases the probability of another.

Additionally, this approach may require careful calibration and understanding of the underlying assumptions. Nevertheless, when utilized wisely, randomization can be a valuable tool in any cyber risk analyst's toolkit.

Risk Tolerance and Loss Exceedance: The Power of Monte Carlo Simulations

Understanding a firm's tolerance for risk and its potential exposure to losses that exceed that tolerance is a critical aspect of cybersecurity management. Monte Carlo simulations can provide deep insights into these areas.

Monte Carlo simulations are named after the famous gambling destination. They use randomness to model the probability of different outcomes. By running hundreds, thousands, or even millions of simulations of total losses during a period, analysts can build a probability distribution called a Loss Exceedance Curve (LEC).

1. Identify Scenarios: Define scenarios to simulate, such as the likelihood of attacks and potential financial impacts from multiple events in a period.

2. Generate Random Scenarios: Construct a forecast data table with a predetermined number of scenarios, each representing a loss event period.

3. Run Simulations: Simulate hundreds or thousands of potential loss scenarios.

4. Build a Frequency Table: Group scenarios into different bins by loss amount. Calculate the cumulative probability that losses will exceed each bin level (for example, by using Excel’s COUNTIF() function).

5. Create a Loss Exceedance Curve: Plot the losses by bin on a line chart, showing the probability of loss amounts exceeding different levels.

Forecast Data Table of Annualized Loss Simulations

(download this template in Excel)

Comparing a Firm's Risk Tolerance with the Loss Exceedance Curve to Assess Aggregate Risk

Risk tolerance is the level of risk that an organization is willing to accept. A firm’s risk tolerance is typically aligned with the its overall objectives, values, and risk management strategy.

The LEC shows the probability that a certain level of losses will be exceeded. The horizontal axis of the LEC shows dollar amounts, and the vertical axis shows the probability that losses will be greater than that amount. The curve descends from left to right.

The LEC can be compared to a firm's risk tolerance to determine if the organization is taking on too much or too little risk (“We’re comfortable with 10% probability of exceeding a $200,000 loss, but not comfortable with 20% probability of exceeding a $200,000 loss”).

If the curve shows a high probability of exceeding the firm's risk tolerance level, adjustments to the risk management strategy may be necessary, such as transferring risk through insurance, mitigating the risk through control activities, or avoiding the risk by discontinuing services in certain markets.

Loss Exceedance Curve

(download this template in Excel)

Determining the firm's risk tolerance relative to metrics like annual revenue may also be useful. For example, if a firm has a risk tolerance of 10% of revenue, and revenue is $10 million, then a 10% probability of losses exceeding $1 million may require the firm to reevaluate its risk management approach.

Benefits and Challenges of Using Monte Carlo Simulations in Cyber Risk Assessment

Monte Carlo simulations offer a powerful way to model complex risk scenarios, such as unlikely combinations of cyber events. By integrating probabilities, they capture a broader range of possibilities and create a more sophisticated understanding of potential loss.

However, the approach may also have its limitations. Accurate simulations require quality data and careful selection of input distributions. The complexity of Monte Carlo models can also be unintuitive for some stakeholders, requiring clear communication about the underlying mechanics and assumptions.

Additional discussion about measuring uncertainty and risk modeling can be found in the following resources:

• How to Measure Anything in Cybersecurity Risk by Douglas W. Hubbard and Richard Seiersen

• Measuring and Managing Information Risk: A FAIR Approach by Jack Freund and Jack Jones

Conclusion

In the rapidly evolving world of cyber threats, organizations should explore innovative tools and methods. Monte Carlo simulations offer a pragmatic yet powerful approach to understanding and managing cyber risk.

By employing these techniques, firms can gain nuanced insights into their risk landscape, align their risk management strategies with their tolerance, and make informed decisions that protect their assets and reputation. As the landscape continues to change, these tools provide a scalable and adaptable solution that can meet the unique needs and challenges of today's digital age.