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Cyber Risk Quantification, FAIR, Cyber Risk Management

A Practical Approach to FAIR Cyber Risk Quantification

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In the ever-evolving world of cybersecurity, managing risk is no longer about simply setting up firewalls and antivirus software. As cyber threats become more sophisticated, organizations require a robust and comprehensive framework to understand and quantify risk in monetary terms. This is where the Factor Analysis of Information Risk (FAIR) Model steps in. Widely regarded as the gold standard for cyber risk quantification, FAIR transforms the often abstract world of cybersecurity risk into a clear, data-driven decision-making process.

In this blog, we will delve into the intricacies of the FAIR Model and its associated ontologies, uncovering how this framework provides a structured, measurable, and financially oriented approach to cyber risk management. Whether new to the concept or looking to deepen your understanding, this blog will guide you through FAIR's core components, terminology, and benefits.

Diving into the FAIR Cyber Risk Quantification Model

The FAIR Model offers a structured approach to identifying, analyzing, and quantifying cyber risk in financial terms. At its core, the model breaks down risk into four key components:

  1. Loss Event Frequency
  2. Loss Magnitude
  3. Primary vs. Secondary Loss
  4. Risk Calculation Formula (Risk = Loss Event Frequency x Loss Magnitude)

By focusing on these quantifiable elements, FAIR provides organizations with a comprehensive framework to evaluate risk consistently and prioritize mitigation efforts based on business impact.

In contrast, NIST 800-30 tends to emphasize qualitative risk assessments. NIST 800-30, for instance, provides a detailed process for conducting risk assessments but relies heavily on qualitative measurements, using scales like "high," "medium," and "low" to categorize risks. While these frameworks offer valuable guidance on identifying and assessing risks, they often need more precision for direct comparison and prioritization in financial terms. FAIR differentiates itself by providing a quantitative approach that directly translates cyber risks into monetary values, making it easier for executives to understand and align cybersecurity investments with business objectives.

The FAIR Model complements frameworks like NIST 800-30 by providing a deeper layer of quantitative analysis. Organizations can use a NIST 800-30 risk assessment to identify risks, establish their risk management strategy, and then apply FAIR to quantify them accurately. This dual approach enables organizations to bridge the gap between technical cybersecurity issues and strategic business decision-making, ensuring that cybersecurity investments are effective and aligned with overall business priorities. Ultimately, FAIR enhances traditional risk frameworks by offering a standardized, financially oriented methodology that resonates with IT professionals and business executives.

CyberStrong offers a flexible, multi-model approach to cyber risk quantification. Download our risk model comparison brief to learn more.

Data for FAIR Cyber Risk Quantification

Now that we’ve established what FAIR is and how the FAIR risk methodology works, we need to identify and collect data sources to input into the calculation. To quantify the impact of the identified risks, you must collect data on the risk factors. There are four elements that you must consider:

Threat Event Frequency (TEF)

This element measures how often a threat event is expected to occur. You can collect this data by reviewing past security incidents and breaches within your organization to estimate how frequently specific threats arise. Analyze patterns in IT support requests to identify recurring security-related issues and monitor logs from firewalls, IDS/IPS systems, and other security devices for attempted attacks.

You can also leverage industry reports and data sets that offer insights into the frequency of various attack types across industries. CyberStrong leverages one of the most extensive cyber loss data sets, updated regularly to deliver the most up-to-date and accurate information.

Vulnerability (VULN)

This element assesses the probability that a threat event will result in a loss event. Use penetration testing, security audits, and past security incident analysis to gauge control effectiveness and identify weaknesses. You can gather additional data by monitoring SIEM systems for patterns indicating successful exploitation attempts and reviewing past incidents and breaches within your organization to determine the frequency of successful exploits.

Contact Frequency

This element estimates how often assets are exposed to potential threats. Analyze the logs for the number of blocked and attempted connections from potentially malicious IP addresses. Review SIEM data for patterns of suspicious network traffic, login attempts, or scanning activities. Consider data from endpoint detection and response (EDR) tools that detect attempts to access sensitive assets.

Additionally, you can refer to vulnerability scanning logs and pen testing reports to review scan logs for indications of scanning attempts and examine pentest results for information on attempted contacts.

Probability of Action (Threat Capability)

Probability of Action (PoA) measures the likelihood that a threat actor will take harmful action after coming into contact with an asset. This probability depends heavily on the Threat Actor's Capability (TCap) and the targeted asset type. Assess threat intelligence reports, understand potential attacker profiles, and evaluate their capabilities and resources relative to the organization's defenses.

Refer to security logs and internal intelligence reports, and analyze findings from Red Team engagements to gauge the threat actor's likelihood of exploiting a vulnerability.

Forms of Loss

As previously stated, the FAIR model has two forms of loss: Primary and Secondary. You can collect data on primary loss by reviewing internal incident records, financial audits, and cyber insurance claims to identify direct costs associated with security incidents. Incident response team reports, legal department records, and historical data from SIEM alerts also provide valuable insights.

Secondary loss accounts for the broader, often indirect consequences of an event. You can collect data on secondary loss by reviewing historical incident records, financial audits, and cyber insurance claims to identify indirect costs like reputational damage, regulatory fines, and litigation expenses. Collaborate with legal, finance, and public relations teams to analyze incident response reports, which can provide insights into legal and regulatory response costs and public relations campaigns. External reports like Ponemon's Cost of a Data Breach and Verizon's DBIR offer industry benchmarks, while customer and employee surveys can reveal potential churn and productivity loss.

Read more about the FAIR Risk Model in this blog, which explains how FAIR can empower CISOs and the Boardroom for strategic cyber risk planning.

Conducting FAIR Cyber Risk Quantification

Once you have collected this data, you will be ready to conduct a FAIR-based risk analysis. While this may seem like an extensive list of data collection, you can streamline FAIR preparation by leveraging a comprehensive cyber risk management platform that handles risk identification and assessment for you.

The CyberStrong platform offers Continuous Control Automation™ (CCA), enhanced reporting, risk benchmarking, and flexible risk quantification capabilities for a comprehensive FAIR implementation. Schedule a conversation to learn about our streamlined approach to FAIR - one of the easiest on the market.

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