Open FAIR Methodology

How Open FAIR Methodology Works

Open FAIR turns cyber risk into a structured loss analysis. Instead of asking only whether a control is “good” or “bad,” it separates a scenario into how often loss events may happen and how large the resulting loss could be, then combines those factors into a quantified view of exposure.

That structure matters because it forces teams to describe assumptions explicitly. A weak control, a threat action, or a vulnerable asset is not just a label in a heat map, it becomes part of a measurable scenario that can be compared with other risks on the same basis.

For readers who want the broader context behind that distinction, Quantitative vs Qualitative Cyber Risk Guide explains why ordinal scores often hide uncertainty, while Open FAIR is built to estimate risk in financial terms.

Frequency and Magnitude in Open FAIR

Open FAIR is usually described through two core dimensions: event frequency and loss magnitude. Frequency estimates how often a loss-producing scenario may occur, while magnitude estimates how severe the loss could be across direct costs, operational disruption, response effort, or other defined impact categories.

The value of this split is that it avoids compressing all uncertainty into a single score. A scenario can be frequent but low impact, rare but catastrophic, or somewhere in between, and the method preserves that difference instead of flattening it into a subjective ranking.

This is also why the model is useful for comparing controls. If a control reduces event frequency, it changes one side of the model; if it reduces impact, it changes the other. The result is a more defensible way to reason about which investment changes the expected loss profile most.

For teams building a risk-finance narrative, Cyber Risk Quantification Guide shows how frequency and loss magnitude can be converted into board-ready analysis.

Why Open FAIR Is Used for Decision-Making

Open FAIR is most useful when leaders need to compare risks, rank treatments, or justify investment decisions using the same unit of measure. It is especially relevant when the question is not “is this a risk?” but “how much risk does it create, and how much would a proposed control reduce?”

That makes it a practical bridge between security teams and finance, operations, and enterprise risk functions. The methodology gives stakeholders a common language for loss exposure, which is often more decision-useful than a list of unrelated findings or a colour-coded matrix.

It also fits scenarios where assumptions need to be challenged. If a team cannot explain how often an event can occur or how loss would be realised, the resulting estimate is likely too vague to support capital allocation or prioritisation.

NHIMG’s IAM Business Case and ROI Guide uses FAIR-style quantification to show how loss analysis can support investment justification, not just risk reporting.

Open FAIR in Modern Cyber Risk Practice

Open FAIR is best understood as a repeatable analysis method, not a one-time scoring exercise. Teams use it to model scenarios consistently, update assumptions as evidence changes, and communicate risk in terms that support treatment choices rather than abstract severity labels.

Because the method depends on inputs, its quality rises or falls with the quality of those inputs. Poorly defined scenarios, vague loss categories, or untested estimates can still produce neat-looking numbers, but they will not produce trustworthy decisions. The discipline is in the modelling approach, not in the spreadsheet.

For organisations building a broader programme around quantified risk, FAIR Model and Open FAIR Standard Guide provides a deeper walk-through of the model structure, while Risk Management Guide places Open FAIR within the wider lifecycle of appetite, treatment, and monitoring.

Risk and Threat Considerations

Open FAIR reduces the risk of decision-making by intuition alone, but it introduces its own failure mode: false precision. If scenario definitions, control effects, or magnitude estimates are weak, the output can look rigorous while still reflecting fragile assumptions.

Failure mechanism: The model can understate or overstate exposure when frequency, vulnerability, or loss estimates are poorly grounded, when scenarios are too broad, or when teams treat a calculated result as certainty instead of a decision aid.

Impact: Misestimated loss exposure can lead to misplaced investment, underfunded controls, or acceptance of risks that are larger than the organisation believes. In practice, the harm is usually not the calculation itself, but the confidence placed in a poorly modelled calculation.

Practitioner Guidance

Why practitioners should care: Open FAIR is most valuable when an organisation needs repeatable, defensible risk estimates for prioritisation, budget decisions, or control trade-offs. Its usefulness depends on whether the team can support the scenario with evidence, not whether the model simply produces a number.

What to watch for: Treat the output as only as strong as the scenario definition and input quality. If different teams are modelling the same exposure with incompatible assumptions, the method will not give comparable results, even if the format looks consistent.

Related resources from NHI Mgmt Group

• How does Open Source Security Testing Methodology Manual (OSSTMM) work?
• How do organisations implement Open Source Security Testing Methodology Manual (OSSTMM)?
• Open Source Security Testing Methodology Manual (OSSTMM)
• Should organisations adopt open standards for authorization now?