In March 2022, Security Magazine published a list of cybersecurity predictions for the upcoming year. This list showed that criminals are constantly improving their craft employing ever-more advanced weapons to attack organizations.

These new-age cybersecurity threats include deep fakes as part of social engineering attacks, threats targeting satellite networks, and Edge-Access Trojans (EATs) that stealthily hijack critical systems, applications, and data.

As such advanced threats proliferate, organizations need tools and solutions to help them better understand the harm of these attacks. It’s not enough to understand threats only in technical terms; CISOs need to understand how those threats would disrupt business operations, so that they and other senior managers can make better decisions about security investments and enterprise risk management.

This is where cyber risk modeling comes in.

Cyber risk modeling (also known as cybersecurity risk modeling) is a way to quantify risk. It allows risk managers and business leaders to estimate the financial cost of a cyber risk, understand the level of exposure, and make sound, data-driven risk management decisions.

how to quantify risks with cyber risk modeling

The Need for Cyber Risk Modeling

Cyber risk is a complex, expanding and costly problem for organizations. In late 2020, cybercrime losses cost the world economy at least $1 trillion, a more than 50 percent increase from 2018 and almost 1 percent of global GDP. By 2025, cybercrime costs are expected to rise to $10.5 trillion.

According to the FBI, cyberattacks have tripled since the start of the COVID-19 pandemic. Google reported a huge jump in COVID-19-related phishing attacks, blocking more than 18 million COVID-related phishing attempts per day. Ransomware attacks also increased by 486 percent from 2019 to 2021, and are expected to cost organizations at least $265 billion by 2030.

In such a risky environment, organizations need robust enterprise risk management to identify, analyze, eliminate, and mitigate cyber risk. They must also understand risk in financial terms to guide their management decisions and investments. Cyber risk models help with all those needs.

An effective cybersecurity risk modeling tool can support an organization’s strategic cyber risk management and decision-making. It allows CISOs and chief risk officers to quantify the financial impact of each risk affecting the organization.

With a cyber risk model, senior leaders and board members can clearly see how a formal cyber security risk management process could help reduce cyber risks. They can also justify how the cybersecurity budget and investments could help the bottom line.

The right cyber risk model is also advantageous because it can help the company:

  • Determine cyber risks and the company’s exposure to each one;
  • Implement and optimize security controls for effective risk management;
  • Understand security gaps and prioritize improvements;
  • Make effective investments to reduce cyber risks.

Challenges of Traditional Cyber Risk Assessment Processes

In one study from Harvard Business Review and PwC in 2020, 74 percent of organizations named cyber risk one of their top three risks. Another 44 percent said they required “better and more granular quantification” of cyber risks.

Still, most companies don’t use cyber risk quantification techniques. The HBR/PwC survey found that less than half of organizations have risk matrices to assess cyber threats. The matrices that do exist are just spreadsheets with risks scored as low, medium or high.

Most organizations still rely on traditional qualitative cybersecurity risk analysis and assessment processes, which can be very subjective and therefore not particularly useful. Often, these processes generate risk scores that business users can’t even understand, much less accept.

Analysts generate these scores using risk assessment templates with predefined risk factors. Since the scores are subjective, they can be interpreted in many ways. This one-size-fits-all assessment approach often produces inconsistent results. It also fails to consider:

  • What the risk information means in the context of the most important cyber risks to the organization;
  • How existing controls work together (or not);
  • Which controls are the best for risk reduction.

These challenges reduce the organization’s true understanding of its risk landscape and risk exposure. Traditional risk assessments also increase the potential for misguided management decisions and allow serious security gaps in the company.

How a FAIR-based Cyber Risk Model Overcomes These Challenges

Cyber risk models that use the Factor Analysis of Information Risk (FAIR) framework reveal specific and actionable information to measure (and mitigate) risk. These models are superior to traditional risk assessment methods because they help decision-makers understand, analyze, and quantify risk in financial terms.

With a FAIR-based cyber risk model, organizations can:

  • Conduct cyber risk analyses, and then implement cyber resilience measures accordingly;
  • Implement effective cybersecurity risk management initiatives;
  • Validate the effectiveness of these measures and initiatives;
  • Quantify the return on cyber investment and improve reporting to stakeholders and the board.

Senior leaders can analyze causal relationships in high-risk scenarios and provide financial estimates for each identified risk. They can also compare the variance between risk appetite and the degree of risk tolerance, to guide their risk management strategies.

The output of a FAIR risk model is not a subjective score of high, medium, or low. Instead, it’s a quantifiable measure of the financial effects of cyber risk over time. Risk managers can then use FAIR to weigh any cyber risk against the company’s risk profile.

FAIR risk models can be used with other risk frameworks such as COSO, COBIT, and the NIST Cybersecurity Framework (CSF). Moreover, they consider the impact of risk on multiple stakeholders, to create a more holistic and complete picture of the organization’s risk profile.

Cyber Risk Model: How It Works and Key Features

A robust cyber risk model would include a set of fully probabilistic and aggregation risk models to capture, understand, and quantify a wide range of attack scenarios.

The model’s analytics engine would accurately model the industry’s specific cyber threats. It would integrate analytics and metrics into internal applications with application programming interfaces (APIs), allowing firms to make better decisions about cyber risk selection, risk mitigation, and risk transfer.

The best risk models analyze historical global-scale ransomware events such as WannaCry and NotPetya and then assess the potential for losses to the organization. They consider the downtime duration of such cyber events and their important “probabilities of infection” (PoIs), such as operating system, poor cyber hygiene, and industry. These data points demonstrate the potential spread, severity, and possible financial impact of serious ransomware events.

Ideally, the risk model would include tens of thousands of security events of varying severity to help companies understand potential losses across multiple key areas, such as:

  • Business interruption;
  • Cyber incident remediation;
  • Data and asset recovery following a data breach;
  • Cyber forensics;
  • Cyber extortion (ransom payments).

Organizations can use cyber risk models for “pricing” individual cyber risks and to measure tail risk. Some models support data enrichment with a global database of millions of organizations, so risk managers can fill in missing exposure characteristics and improve the model’s accuracy.

Cutting-edge cybersecurity risk models incorporate cyber risk assumptions and provide insights into the processes that might affect enterprise assets and technology. They also allow companies to customize the model by incorporating their own risk view, adding user-defined alternative parameters, and adjusting severity to match their specific risk needs.

Cyber Risk Modeling vs. Cyber Threat Modeling

The terms cyber risk modeling and cyber threat modeling are often used synonymously, but they are different ideas. Cyber risk modeling involves creating multiple risk scenarios and assessing the severity of each.

Risk modeling provides a data-driven approach to understand cyber exposure and to quantify the possible outcome if a risk does indeed strike. This information is documented and disseminated in a language that makes sense to business users and decision-makers.

A cyber risk model – particularly one that uses the same tools available to the cyber insurance sector – provides an efficient and repeatable way to quantify the probability of a cyberattack in financial terms.

On the other hand, a threat model helps to identify cyber threats and vulnerabilities. It also informs the company’s response and mitigation efforts.

Manage Risk and Reduce Business Exposure with Reciprocity ZenRisk

A cyber risk model is vital to understand different risk scenarios through a financial lens. To see and understand the various risks affecting your organization, a platform such as ZenRisk is crucial.

ZenRisk provides customizable risk calculations to evaluate risks across connections. It also provides heatmaps and dashboards to address threats and communicate risk information to stakeholders.

Take advantage of ZenRisk’s automated workflows to remediate risks quickly and implement continuous risk monitoring workflows. With advanced automation, a single source of truth, and predefined templates, ZenRisk can be a valuable asset to your enterprise risk management methodologies.

Schedule a demo to learn more about ZenRisk’s world-class capabilities.