Factor Analysis of Information Risk (FAIR) is designed to manage vulnerabilities and incidents within an organization, network, or system using a risk-based approach. The main strength of the FAIR risk framework is the use of numerical values, mathematics and quantification to get precise and accurate results and responses.
Information security has increased in importance because of the growing number of threats that have emerged daily. Compliance with government regulations is no longer enough. It can only provide minimum security, but it cannot keep up with cybersecurity’s ever-evolving complexity.
The FAIR risk management framework is in place to give sufficient protection against these threats by managing the probability of their frequency and magnitude. It has become an effective shield to protect the operations of organizations and businesses.
Let this guide help with implementing this framework for better decision-making and loss mitigation.
When Is the Best Time to Use FAIR Risk Framework
Risk always exists. This is why organizations that strive to protect its assets must not be complacent. The best time to use the FAIR risk management framework is 24/7 implementation.
This framework has a variety of applications to help its intended beneficiaries. Here are several examples wherein FAIR has proven useful:
- Audit results
- Penetration findings
- Comparisons of risk magnitude
- Requests for policy exception
- Proposal defense for security measure upgrades
- Justification of existing security costs
- Determination of risk mitigation priorities, especially when the budget is limited
- Optimization of cyber defense
Understanding the Risk Model Components
The first step in implementing the FAIR risk framework is to understand its functions and capabilities. FAIR is designed to express risk probabilities in a quantifiable manner.
If it can be measured, it can be anticipated and analyzed.
Thus, the components of the risk model are all geared towards quantification. Consider the following purposes of FAIR risk management:
- Creation of taxonomy and ontology that classifies the various aspects of information and operational risk
- A method for gathering data and analyzing its findings
- A scale to determine the magnitude and frequency of risk
- A model constructs to make sense of complex risk situations
- Integration of data findings into computational engines and software
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The Quantitative Scale
A central key to the FAIR framework’s success is the numerical data that lead to quantitative analysis.
Typically, this risk analysis manner does not use ordinal numbers such as 1 to 10 to gauge a spectrum of risk possibilities. It expresses numerical values in levels such as High, Moderate, or Low. FAIR uses these units to describe important risk terms such as frequency, ratio, magnitude and financial loss.
Take reputational loss as a case study. As a concept, it is difficult to measure. But with the help of the FAIR framework, there is a way to quantify it.
In the commercial industry, a reputational loss can be expressed in decreased stock prices, capital cost, or reduced market share.
The public sector can come in the form of subject matter range estimates related to mission and vision. Executives typically have an understanding of market competition, consumer demand and other business factors.
Analysis Output
Powered by data gathering, one of the FAIR risk framework’s best attributes is to analyze risk with hard facts.
The FAIR risk analysis method combines the taxonomy of threats, calibrated scenario estimates from organization leaders, PERT or program evaluation and review techniques, and the Monte Carlo stochastic simulation.
The result is a perfect storm of findings that estimate the frequency and magnitude of losses to assets. It is best expressed in a table format.
Among the analytics that emerges from these data presentations include the following:
- Primary loss magnitude
- Total loss exposure
- Primary losses per year
The Specifics of the FAIR Framework
With more information gathered, more understanding will emerge and these can lead to informed decisions. Focusing on data gathering, FAIR can help the decision-makers in an organization with the right data to help mitigate, prevent and respond to risks and losses.
A robust FAIR risk framework that stands the test of time can help provide a deeper level of protection with the following benefits:
- A more comprehensive ontology and taxonomy of threats
- More control analysis models
- Quantitative analysis model to gauge the capability of an organization to manage risk over time
- Better distribution models for numerical variables instead of matrices and scales
- The Monte Carlo function for highly uncertain information risk
- Sensitivity analysis for locating risks in scenarios
- Calibration for data gathering improvement, especially when data are scant
- Risk aggregation
Increased features will come out of these complexities. But it may become more challenging to understand as it becomes more complicated. To take advantage of the advanced features of the FAIR framework, the use of calibrated software is recommended. This will significantly assist risk analysts and organization leaders in making recommendations and decisions.
The Monte Carlo Simulation
A vital mathematical process that is integral in the FAIR risk framework is the Monte Carlo simulation. This strategy is deployed to create a probability model that maps different outcomes in a process that cannot be predicted easily because of random variables.
This technique aims to gauge the impact of a potential uncertainty or risk by doing a forecasting model. It can help address problems from various industries such as science, supply chains, finance, marketing and engineering.
An alternative name is multiple probability simulation.
Its central methodology is the assignment of several values to an uncertain variable. After results are acquired, the average will be listed down to get a well-informed estimate. This simulation has an assumption in place that it is gauging an efficient market.
RiskLens: The Software for FAIR Implementation
Complex mathematical processes such as the Monte Carlo simulation may be challenging when done on your own. For these cases, the assistance of software is tactical and practical.
As the FAIR Institute’s official technical advisor, RiskLens has optimized, creating a cyber risk quantification and management system. This digital platform is tailor-made for the FAIR risk management framework and has integration for the following:
- Advanced Value at Risk (VaR) analytics
- Maturity models
- Practice workflows based on templates
- Loss of data specific to industries
- Data integration for business information security
FAIR is a globally recognized set of implementation guidelines for risk quantification, testing and adoption. RiskLens is the only enterprise software platform built for this purpose, making it an essential addition for over 7,000 cybersecurity professionals that operate under the FAIR framework.
This network of risk analysts shares resources daily, enabling them to offset the growing number of cybersecurity threats that evolve worldwide. The Fair Institute has eight organization members that are part of the Top 10 Fortune companies. Seventy-five percent of the Fortune 50, 33 percent of the Fortune 100 and 30 percent of the Fortune 1,000 are all affiliates.
Acquiring this software platform is an essential step in the implementation of the FAIR risk framework.
A Sample Application
With all the assessment and computations complete, the next step is to carry out the FAIR risk management framework in the organization.
It may become complicated because of the technical aspects of the framework. But it is not designed for simplicity. It was created to get accurate and precise findings that will be used for informed decisions and the creation of contingencies. There are four stages to this undertaking:
Stage 1: Scenario Identification
The various components that comprise a scenario at risk should be identified clearly and systematically. It is essential to pinpoint the assets that may be compromised.
This is also the right timing to identify the threat agent or community that will likely cause the problem.
Stage 2: Data Crunch
With the gathered data, it is essential to identify the various numerical values necessary in decision making. They include the following:
- Loss Event Frequency (LEF)
- Threat Event Frequency (TEF)
- Threat Capability (TCap)
- Estimate Control Strength (CS)
- Vulnerability (Vuln)
- Derivation of Loss Event Frequency
Stage 3: Probable Loss Magnitude
This is the phase wherein the worst-case loss scenario must be identified. It is also the perfect opportunity to create informed decisions about the dangers of a potential threat.
Stage 4: Articulate Risk
With due diligence done in data gathering, it is now vital to make sense of the severity. This is the phase to discuss steps to mitigate, lessen, or prevent these risks from happening and disrupt the organization’s operations.
Global Approval
The FAIR Risk Framework’s reliability has been tried and tested with approval from the leading authority figures regarding information risk and cybersecurity. Various aspects of its development have overcome the rigid scrutiny of risk analysis experts, making it a reliable risk management system.
The Open Group
Foremost of these authorities is the Open Group, a global consortium that has declared FAIR as its primary model for risk management and quantification. They have written and published the FAIR-ISO/IEC 27005 Cookbook that outlines the application of the FAIR risk model to any management framework.
The Open Group expressed their belief in the FAIR risk model as the perfect complement to other risk assessment frameworks such as ISO/IEC 27002, OCTAVE, ITIL, COSO and many more. With the help of RiskLens, the FAIR framework provides an engine that can improve risk assessment results and be used in other risk models.
The PCI Data Security Standard
Section 3 of the Risk Assessment guidelines of the Payment Card Industry Data Security Standard (PCI DSS) recommends full compliance with the standard risk methodologies of the ISO or NIST. For further protection, they have also recommended using the FAIR risk framework as a supplement to the global standards.
(ISC)²
The International Information System Security Certification Consortium or (ISC)² has also given its full endorsement of the FAIR framework, particularly its standard taxonomy of threats.
This ontology helps members of the consortium articulate the cybersecurity and information risk inherent in everyday life. It is also useful in creating models for quantification and assessment of these risks.
Various Government Agencies
From a global standpoint, the sustained success of the FAIR framework has earned the attention of several government offices that are looking to bolster their cybersecurity. This aspect is vital because personal — and often, financial — citizens are at risk if left unprotected.
Some of these government agencies are looking to develop a consistent and reliable methodology to anticipate, measure and prevent cybersecurity risks. They include the Federal Deposit Insurance Corporation (FDIC), the Federal Reserve Board and the Office of the Comptroller of the Currency (OCC).
The FAIR framework has emerged as a very viable solution for these concerns.
Expert Assistance for FAIR Risk Framework Implementation
For the uninitiated, Factor Analysis of Information Risk may seem complicated. The stakes of information security are too immense to leave everything up to chance. For the best robust implementation of the FAIR risk management framework, it is best to seek professional guidance.
Look no further than RSI Security for a meaningful partnership when implementing the FAIR risk framework. Our extensive body of work will provide your organization with a reliable layer of security and peace of mind regarding cybersecurity and technical proficiency.
We have a team of experts that can provide inputs and a steady hand while the FAIR framework is implemented in your organization. This risk-based approach will entail a lot of quantitative values and technical values. RSI Security will see to it that your organization will have the best framework in place to meet its operational and cybersecurity needs.
