By Jack Freund
Using the issue research of knowledge probability (FAIR) method constructed over ten years and followed by means of firms around the world, Measuring and coping with info Risk offers a confirmed and credible framework for knowing, measuring, and studying details threat of any dimension or complexity. meant for enterprises that have to both construct a threat administration application from the floor up or enhance an present one, this e-book offers a special and clean standpoint on the right way to do a simple quantitative probability research. masking such key parts as possibility thought, possibility calculation, state of affairs modeling, and speaking possibility in the organization, Measuring and coping with info Risk is helping managers make higher company judgements through knowing their organizational risk.
- Uses issue research of data chance (FAIR) as a technique for measuring and handling threat in any organization.
- Carefully balances concept with useful applicability and proper tales of profitable implementation.
- Includes examples from a wide selection of companies and occasions provided in an obtainable writing style.
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Extra resources for Measuring and managing information risk : a FAIR approach
2 FAIR top-level ontology. TALKING ABOUT RISK As is true for many words in the English language, there are multiple definitions for risk. Rather than engage in a religious debate about which is the right definition, we want to make it clear that FAIR is strict in its use of the word risk. If you feel strongly that “risk” means something other than exposure to loss, then feel free to mentally substitute the term “loss exposure” any time you see the word “risk” in this book. Loss Exposure appears to generate less controversy, which can help refocus dialogue away from open-ended and passionate debates.
The point we’re trying to make here is that both measurement methods were repeatable, just to different degrees of precision. This notion of a useful degree of precision is key because we are making a measurement in order to facilitate a decision. ” When dealing with risk, particularly information security related risk, high degrees of precision are a pipe dream in the vast majority of analyses. The aim of analyses should be to be better informed in our decision making through better models, assumptions that are more clearly understood, and better use of whatever data we have.
As a result, the only time we account for the losses incurred by secondary stakeholders is when and if those losses are going to flow through to the primary stakeholder. 6 FAIR loss magnitude ontology. loss component of the analysis. Note that we can always do a separate risk analysis from the public’s perspective if that were useful to us. 6 (primary loss magnitude (PLM), secondary risk (SR), secondary loss-event frequency, and secondary loss magnitude (SLM)) represent a logical breakdown of how loss works computationally.
Measuring and managing information risk : a FAIR approach by Jack Freund