What is The FAIR Methodology In Risk Management?

What is The FAIR Methodology In Risk Management?

Organizations worldwide are progressively shifting to risk-based approaches for guiding the implementation of information security measures to safeguard their data. Compliance frameworks provide good security guidelines, and with proper risk management planning, organizations can do a good job of adequately protecting sensitive information.

Recently, as most corporate processes have been digitalized, information risk has become a business concern rather than a technical issue. Boards of directors and corporate leaders want to understand an organization’s financial loss exposure to make informed decisions. This is where risk management experts come in, primarily responsible for balancing organizational protection and business operations. 

Cybersecurity standards such as NIST CSF and ISO 27005 are non-prescriptive, providing an inherently subjective qualitative method for practitioners to measure risk. The  FAIR Institute, a professional non-profit organization, introduced the solution to this in the form of the FAIR methodology.

The FAIR method provides a risk analysis model that is objective and quantitative, resulting in a mathematical risk estimate. This results in creating risk scenarios that can be compared to one another. Considering everything, analysts and information security specialists have all the information they need to develop effective cyber risk prevention methods with the FAIR methodology. In this blog, we will give you a brief overview of the FAIR framework, discuss the FAIR methodology’s different stages, and understand this method’s benefits and drawbacks.  

What is the FAIR Methodology?

The Factor Analysis of Information Risk or FAIR standard provides a taxonomy and methodology for analyzing cyber risk across all corporate operations. The FAIR architecture connects cybersecurity professionals, business managers, and general management through monetarily quantifiable risk scenarios. 

This approach to cyber risk analysis initially presents a taxonomy of the many risk elements and a set of definitions that define specific concepts: risk, threat, danger, asset, control, and audit. The FAIR technique demonstrates the links between these aspects, providing a corporation with food for thought.

The FAIR standard also includes a technique for categorizing risk into different measurable variables and quantifying risk using statistics and probability. The goals are to examine complicated hazards, discover critical data for quantification, and comprehend the interdependence of risk components.

Forecasts of future loss (in USD, GBP, EUR, or other currencies) can then be offered to decision-makers based on rational, easy-to-explain, repeatable, and defensible scenarios.

Conducting a FAIR risk analysis does not just reveal a company’s vulnerabilities. Organizations can better prioritize their cyber defense efforts, select cost-effective solutions, and improve the return on investment of their cybersecurity equipment, owing to the FAIR methodology.

Breaking Down the FAIR Framework

The FAIR model provides a framework for breaking down risk into quantifiable variables and estimating risk quantitatively using statistics and probability. The goal is to analyze well-scoped risk scenarios, collect data for quantification, and comprehend the link between these risk components.

The FAIR framework is more probabilistic than predictive. As a result, risk is defined as “the probable frequency and magnitude of future loss.” FAIR, in other words, evaluate risk using a combination of Loss Event Frequency (LEF) and Probable Loss Magnitude (PLM), where:

Risk (in $$$) = Loss Event Frequency (LEF) * Probable Loss Magnitude (PLM – in $$$)

A loss’s frequency and magnitude are linked to an asset. According to FAIR, identifying an investment and its worth is critical to defining and quantifying risk. An asset is any device, data, or other organizational components with intrinsic worth that can be altered in a way that results in a loss. FAIR considers the following losses when calculating the value of an asset:

  • Productivity – Losses incurred by an organization due to its failure to produce critical products and services.
  • Response – Resources spent promptly responding to a risk or threat.
  • Replacement – Cost of replacing any damaged assets.
  • Reputation – Missed opportunities or sales due to a dwindling shareholder or brand image.
  • Competitive Edge – Missed chances or the expenses of losing a competitive advantage, such as intellectual property or market share.
  • Legal penalties – Costs of legal processes or fines incurred due to the threat incident.

The FAIR framework refers to a threat agent or threat community as an event or scenario capable of acting against an asset in a way that can result in any of the abovementioned losses. Examples may include a natural disaster or a malware assault on a network.

In the following section, we will see what the FAIR model looks like.

The FAIR Model: A Brief Overview

Illustrated below is the FAIR Model as a diagram:

For an organization to implement the FAIR method properly, it is essential to understand the different terms in greater detail. For starters —

Loss Event Frequency (LEF): This estimates the number of times a given loss event will likely occur within a definite timeframe. Loss event frequency can be broken down into two factors:

  • Threat Event Frequency (TEF) – The number of times a threat or risk might occur, and,
  • Vulnerabilities (Vul) – The probability that a threat will result in a loss event.

These branches aim to quantify events that could create a risk and the likelihood that they will occur. The following information further influences this calculation:

  • Contact Frequency (CF) – The frequency with which an asset comes into contact with a threat;
  • Probability of Action (PoA) – The likelihood that a threat will act against an asset if it comes into contact with it;
  • Threat Capability (TCap) – The amount of force a threat can use against an asset given its skills and resources; and,
  • Resistance Strength (RS) – The asset’s ability to withstand a threat’s attempts to compromise it.

On the other hand, we have Loss Magnitude (LM), which contains the factors that influence loss magnitude when threat events occur—Primary and Secondary, defined as follows:

Primary Loss (PL) – The direct loss experienced by the major stakeholder due to a threat event.

Secondary Loss (SL) – The loss suffered by the primary stakeholder due to the secondary stakeholders’ negative reaction to the loss event.

Ultimately, the FAIR model assists in reducing the likelihood of risks by recognizing the elements that contribute to them. Organizations are left with two options for lowering loss exposure across the organization: limiting the number of times a loss event occurs or mitigating the financial losses these events would cause.

What are the Stages of the FAIR Methodology?

Now that you understand the FAIR approach better, we will focus on the four stages of FAIR implementation at an organization. Check them out.

  1. Stage 1 – Identify Scenario Components

In this stage, identify the asset at risk and the sources of threat agents or communities being considered.

  1. Stage 2 – Assess Loss Event Frequency (LEF)

This stage collects data and estimates several attributes essential to the methodology, such as Threat Event Frequencies (TEF), Threat Capability (TCap), Resistance Strength (RS), Vulnerability (Vul), and Loss Event Frequency (LEF).

  1. Stage 3 – Evaluate Probable Loss Magnitude (PLM)

This stage determines how much loss an organization may anticipate from a primary or secondary loss event and the scale of impact that a threat event will have within and beyond the organization. This is also where companies may estimate their losses in the worst-case scenario.

  1. Stage 4 – Comprehend and Articulate Risk

This stage focuses on categorizing risk variables, measuring these factors and their associated loss, and creating a computational model that depicts the link between these discovered factors. It also entails analyzing risk situations and determining their likely financial impact using a simulation model.

Benefits and Drawbacks of Implementing the FAIR Methodology

Finally, in this section, we will highlight the benefits and drawbacks of implementing the FAIR method to measure risk in your organization.

Benefits of the FAIR Methodology

The FAIR approach converts cybersecurity risk into business terms. There is even a FAIR taxonomy that gives business users and leaders clear, actionable explanations of cybersecurity risk. With FAIR, organizations get

  • Threat Protection: FAIR threat modeling can be used to create models and analyze complicated cyber threat scenarios.
  • Growth Facilitation: The FAIR architecture enables fast-growing businesses to adapt to cyber threats at any stage.
  • Business Adaptability: FAIR is a flexible framework that gives users insights into attack prevention methods.
  • Cost Effectiveness: With FAIR, companies can make cost-effective judgments by understanding the financial impact and ROI of each measure.

Drawbacks of the FAIR Methodology

In general, stage 1 of FAIR comprises a group of people appraising a collection of considered dangers and their expected loss, as well as filling out a questionnaire in a FAIR tool based on their qualitative knowledge. This stage comes with the following disadvantages:

  • High-cost and time-consuming;
  • Necessitates a thorough understanding of the FAIR risk ontology;
  • Requires manual data gathering;
  • Needs input from scenario-related experts or consultants; and,
  • It uses subjective data, which might lead to mistakes.

In stages 2 and 3, where risk security experts need to gather information based on defined risk scenarios and then predict Loss Event Frequency and Probable Loss Magnitude, you may come across a lot of useless information that generates a false impression of confidence. This is mainly due to an overreliance on probability estimation.

When it comes to stage 4 of the FAIR method, where risk security professionals employ a simulation model to analyze, articulate, and derive the likely financial impact of risk, it:

  • Does not identify vulnerabilities or recognize concrete activities that a company can take to strengthen its cybersecurity posture and lessen the chance of a breach;
  • Does not provide remedial guidelines that security teams can employ to swiftly and efficiently handle attacks across their whole network since security measures are specified for individual risks only; and,
  • It makes it difficult to operationalize continuous and repeatable cleanup and mitigation of cyber risks.

FAIR’s drawbacks mainly stem from the non-availability of detailed cybersecurity data from your company’s infrastructure. However, times have changed, and new threats and vulnerabilities can be detected regularly in the digital age. Moreover, configuration changes, patches, upgrades, new code releases, and so on are a constant component of today’s technology scene. Thus, these limitations are fairly uncommon today.

However, Organizations need to keep one thing in mind; the FAIR methodology is a probabilistic approach, which means it does not allow for prediction (no method does. It does not aim for exhaustiveness but focuses on the assets most vital to an organization’s operation. Therefore, you must carefully evaluate your unique business needs before implementing the FAIR methodology.

Security, Compliance and Risk Management with Akitra!

While the FAIR methodology may provide you with the most accurate assessment of cyber risk-induced potential loss based on available data, compliance frameworks should not be ignored in the larger scheme of safeguarding your organization’s data. Establishing trust is a crucial competitive differentiator when courting new SaaS businesses in today’s era of data breaches and compromised privacy. Customers and partners want assurances that their organizations are doing everything possible to prevent disclosing sensitive data and putting them at risk, and compliance certification fills that need.

Akitra offers an industry-leading, AI-powered Compliance Automation and Risk Management platform for SaaS companies. Using automated evidence collection and continuous monitoring, together with a full suite of customizable policies and controls as a compliance foundation, our compliance automation platform and services help our customers become compliance-ready and certified for security and compliance frameworks like SOC 1, SOC 2, HIPAA, GDPR, PCI DSS, ISO 27001, ISO 27701, ISO 27017, ISO 27018, NIST CSF, NIST 800-53, NIST 800-171, FedRAMP, CCPA, CMMC, and more such as CIS AWS Foundations Benchmark, etc. In addition, companies can use Akitra’s Risk Management product including FAIR methodology for overall risk management for your company, Trust Center, and AI-based Automated Questionnaire Response product to streamline and expedite security questionnaire response processes delivering huge cost savings. Our compliance and security experts will provide customized guidance to navigate the end-to-end compliance process confidently. 

The benefits of our solution include enormous savings in time, human resources, and cost savings, including discounted audit fees with our audit firm partners. Customers achieve compliance certification fast and cost-effectively, stay continuously compliant as they grow, and can become certified under additional frameworks using a single compliance automation platform.

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