Open FAIR: Quantifying Information Risk for Decisions
An overview of the Open FAIR taxonomy and quantitative risk analysis, including loss-event frequency, loss magnitude, uncertainty and decision use.
Key Takeaways
- ✓Define a decision, scenario, scope and time horizon before quantifying.
- ✓Estimate loss-event frequency and magnitude using evidence and ranges.
- ✓Record assumptions, dependencies, confidence and uncertainty.
- ✓Use sensitivity analysis to compare options and treatment effects.
- ✓A quantified estimate is not a certainty or substitute for risk ownership.
Purpose
Open FAIR is a risk analysis method and taxonomy for analyzing information risk quantitatively. It helps teams describe risk factors consistently and estimate the frequency and magnitude of loss events. Quantification can support comparison and investment decisions when assumptions, ranges and uncertainty are made explicit.
Structure an analysis
Define the decision, asset or business objective, scope, time horizon and loss-event scenario. Identify threat activity, vulnerability and the conditions that could lead to loss. Estimate frequency and magnitude using evidence, expert judgment and defensible ranges; record sources, confidence and dependencies. Avoid false precision: the result is an analytical estimate, not an observed fact.
Use the result responsibly
Compare scenarios, test sensitivity to key assumptions and show how proposed treatments could change frequency or loss magnitude. Communicate the range and uncertainty to decision makers, not just a single number. Revisit estimates when control performance, threat conditions, business context or evidence changes. Integrate the analysis with enterprise risk registers and financial planning where useful.
Boundaries
Open FAIR does not prescribe risk appetite, guarantee a forecast or replace legal, safety or compliance analysis. Quantified outputs depend on scope, data and assumptions. Keep the model transparent and avoid presenting a point estimate as certainty.
Frequently asked questions
Does Open FAIR predict exact future losses?
No. It supports structured estimates with uncertainty; results depend on evidence, assumptions and scope.
What should a FAIR analysis quantify?
It models factors related to loss-event frequency and loss magnitude for a defined scenario.
Can FAIR make the risk decision automatically?
No. Decision makers set objectives and tolerances, evaluate evidence and choose responses.
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