Factor analysis of information risk (FAIR for short) is a taxonomy of the factors that contribute to risk and how they affect each other. It is primarily concerned with establishing accurate probabilities for the frequency and magnitude of loss events. It is not, per se, a “cookbook” that describes how to perform an enterprise (or individual) risk assessment.[1]
A number of methodologies deal with risk management in an IT environment or IT risk, related to information security management systems and standards like ISO/IEC 27000-series.
The unanswered challenge, however, is that without a solid understanding of what risk is, what the factors are that drive risk, and without a standard nomenclature, we can’t be consistent or truly effective in using any method. FAIR seeks to provide this foundation, as well as a framework for performing risk analyses. Much of the FAIR framework can be used to strengthen, rather than replace, existing risk analysis processes like those mentioned above.[1]
FAIR is not another methodology to deal with risk management, but it complements existing methodologies.
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As a standards body, The Open Group aims to evangelize the use of FAIR within the context of these risk assessment or management frameworks. In doing so, The Open Group becomes not just a group offering yet another risk assessment framework, but a standards body which solves the difficult problem of developing consistent, defensible statements concerning risk.[1]
ISACA in its Risk IT Framework, that extends COBIT, cites FAIR and its concepts.
The "Build Security In" initiative of Homeland Security Department of USA, cites FAIR. [2]
FAIR main document is "An Introduction to Factor Analysis of Information Risk (FAIR)", Risk Management Insight LLC, November 2006;[3]
The contents of this white paper, and the FAIR framework are released under the Creative Commons Attribution-Noncommercial-Share Alike 2.5. In order to reasonably discuss the factors that drive risk, the document first define what risk is. Risk and Risk Analysis discusses risk concepts and some of the realities surrounding risk analysis and probabilities. This provides a common foundation for understanding and applying FAIR. Risk Landscape Components briefly describes the four primary components that make up any risk scenario. These components have characteristics (factors) that, in combination with one another, drive risk. Risk Factoring begins to decompose information risk into its fundamental parts. The resulting taxonomy describes how the factors combine to drive risk, and establishes a foundation for the rest of the FAIR framework.
The Controls section briefly introduces the three dimensions of a controls landscape. Measuring Risk briefly discusses measurement concepts and challenges, and then provides a high-level discussion of risk factor measurements.
FAIR [3]underlines that risk is an uncertain event and one should not focus on what is possible, but on how probable is a given event. This probabilistic approach is applied to every factor that is analysed. The risk is the probability of a loss tied to an asset.
An asset’s loss potential stems from the value it represents and/or the liability it introduces to an organization.[3] For example, customer information provides value through its role in generating revenue for a commercial organization. That same information also can introduce liability to the organization if a legal duty exists to protect it, or if customers have an expectation that the information about them will be appropriately protected.
FAIR defines six kind of loss:[3]
FAIR defines value/liability as:[3]
Threat agents can be grouped by Threat Communities, subsets of the overall threat agent population that share key characteristics. It’s important to define precisely threat communities in order to effectively evaluate impact (loss magnitude).
Threat agents can act differently on an asset[3]:
This actions can affect differently various asset: the impact is different along with the characteristics of the asset and its usage. Some assets have high criticality and low sensitivity: deny access has a much higher impact than disclosure on them. Vice versa high sensitivity data can have low productivity impact while not available, but huge embarrassment and legal impact if disclosed: former patient health data availability do not affect an healthcare organization productivity but can cost millions dollars if disclosed. [4] A single event can involve different assets: a [laptop theft] has an impact on the availability of the laptop itself but can lead to the potential disclosure of the information stored on it.
The point is that it’s the combination of the asset and type of action against the asset that determines the fundamental nature and degree of loss.
Important aspects to be considered are the agent motive and the affected asset characteristics.