{"text_format":"1","workbook":"xlsx","n_sheets":18,"sheets":["START HERE","Instructions","Scenario Setup","FAIR Model","LEF","Susc","TCap","RS","TEF","CF","PoA","PL","SLEF","SLM","Risk Report","Beta PERT & Monte Carlo","Test Data Generator","Data Migrator"],"not_rendered":{"charts":10,"images":3,"macros":1,"external_links":0,"defined_names":147,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900"}

{"sheet":"START HERE","index":0,"n_rows":8,"n_cols":5,"rows_kept":8,"cut":false,"hidden":false,"merged":[[1,2,1,4],[3,1,3,2],[5,1,6,4],[7,3,10,4]]}
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[null,null,"START HERE\n© FAIR Institute. All rights reserved. "]
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[null,"Workbook Version: 1.3 (Beta)                    [Got Feedback: Email us at FAIRU@FAIRInstitute.org]",null,null,"Release Date: April 28, 2025"]
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[null,"License:\nThis FAIR-U for Learners Workbook is designed to support educating (learning needs) FAIR practitioners, students, and others who want to better understand FAIR and its applications. To facilitate this purpose, this work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License (available at https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode).\n\nTo clarify the application of this license:\n\n     •     You are authorized to copy and redistribute this workbook for use by yourself, within your organization, and outside of your organization, strictly for non-commercial purposes, provided that:\n             1.  Appropriate credit is given to the FAIR Institute as the source.\n             2.  A link to the license is provided.\n     •     You may not remix, transform, or modify this workbook, nor distribute derivative materials based on it.\n     •     Users of the FAIR-U for Learners Workbook should refer to https://www.fairinstitute.org/fair-u-workbook when citing or referencing the workbook to ensure alignment with the most up-to-date guidance.\n     •     Any commercial use of this workbook requires prior approval from the FAIR Institute.\n\nBy using this workbook, you agree to these terms as outlined in the Creative Commons BY-NC-ND 4.0 license.\n\nDisclaimer:\nThe FAIR-U Workbook for Learners is provided “as-is” for educational purposes only. It is designed to help individuals explore and understand the Factor Analysis of Information Risk (FAIR) model through structured exercises and guided analysis.\n\nBy using this workbook, you acknowledge and agree that:\n     •     The workbook is not intended for use in real-world risk analysis, decision-making, or commercial applications.\n     •     The workbook is not warranted to be accurate, complete, or suitable for any specific purpose.\n     •     The FAIR Institute assumes no responsibility or liability for any outcomes, errors, or decisions made based on the use of this workbook.\n     •     All macros and formulas included in this workbook are provided for convenience and should be reviewed before enabling or using them. Users may disable macros at their discretion.\n\nThe FAIR Institute may update or discontinue this workbook at any time without notice. Users should refer to www.fairinstitute.org/fair-u-workbook for the latest version and updates."]
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[null,null,null,"In clicking this button, the workbook will reveal all worksheets and take you to the instructions tab to begin performing a scenario risk assessment. Also, in taking any act to disable macros or protections (locked cells or sheets) or otherwise reveal the worksheets in this workbook, you agree to the terms above."]

{"sheet":"Instructions","index":1,"n_rows":6,"n_cols":5,"rows_kept":6,"cut":false,"hidden":true,"merged":[[5,1,45,4],[1,2,1,4],[3,1,3,2]]}
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[null,null,"INSTRUCTIONS\n© FAIR Institute. All rights reserved. "]
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[null,"Workbook Version: 1.2 (Beta)                    [Got Feedback: Email us at FAIRU@FAIRInstitute.org]",null,null,"Release Date: March 14, 2025"]
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[null,"The FAIR-U Workbook for Learners has been developed as an educational tool to help cyber risk analysts, GRC leaders, CISOs, and other risk professionals better understand the FAIR Model, including its mathematical underpinnings. \nThe Workbook is provided for learning purposes only and is not warranted for commercial use.\n\nLimitations of the Workbook:\nThis workbook is limited to performing 10,000 Monte Carlo simulations (whereas commercial tools typically perform 50,000+ simulations). Furthermore, the workbook is designed for risk scenarios where each loss event is independent—meaning that the occurrence of one event does not influence the likelihood or impact of future events. \n\nStructure of the Workbook:\nNOTE: The workbook and its individual sheets are protected (locked) so that users are only able to edit cells highlighted in yellow. Sheets with fields that users may edit/enter are as follows:\n\nScenario Setup: For users to enter basic facts about the risk scenario.\nFAIR Model: Users should select at which level they want to enter LEF (left-side of the model) factors. Also, users may navigate from the model to any of the underlying input sheets.\nLEF: For users to enter Loss Event Frequency assumptions.\nTEF: For users to enter Threat Event Frequency assumptions.\nSusc: For users to enter Susceptibility assumptions.\nCF: For users to enter Contact Frequency assumptions.*\nPoA: For users to enter Probability of Action assumptions.*\nTCap: For users to enter Threat Capability assumptions.*\nRS: For users to enter Resistance Strength assumptions.*                    *NOTE: It is very rare for analysts to estimate CF, PoA, TCap and RS directly.  However, it is included in this workbook for the purpose of learning the model.\nPL: For users to enter Primary Loss assumptions.\nSLEF: For users to enter Secondary Loss Event Frequency assumptions.\nSLM: For users to enter Secondary Loss Magnitude assumptions.\n\nWhere to Begin:\nBegin by completing the Scenario Setup sheet. Not all fields are required for the analysis. \nOn the FAIR Model sheet, choose the level at which you want to enter LEF factors. This will depend on the data that you have for your analysis. The workbook supports entering data at the lowest level of the model (e.g., CF, PoA, TEF, and TCap), at higher levels (up to LEF), or a combination (e.g., CF + PoA and Susc). You may change the level you wish to enter at any time by selecting a different option in the FAIR Model sheet. For the right side of the model, you will need to enter data for SLEF, SLM, and PL.\n\nWant to create a quick, pre-defined scenario? Use the Test Data Generator on the last sheet. You can review and change any of the pre-defined scenario values, hit the \"Generate Text Scenario\" button, select options #1, #2, or #3, and it will popoulate the entire workbook. It's a good way to quickly see how the model works. (At the FAIR Institute, we use this for testing out workbook but decided to expose it to you as well.)\n\nGuidance is provided on every sheet to help a learner understand the FAIR Model and how to set values (assumptions).\n\nCalculations and Reporting:\nThe workbook does not automatically recalculate the model: AFTER EVERY CHANGE, you must hit the “Recalculate Workbook” button found on most of the sheets.\nAfter recalculating, the workbook will update the FAIR Model, charts, warnings, and other values. To see the underlying calculations, visit the Beta PERT & Monte Carlo sheet.\nFor some simple reports, visit the “Risk Report” tab."]

{"sheet":"Scenario Setup","index":2,"n_rows":73,"n_cols":17,"rows_kept":73,"cut":false,"hidden":true,"merged":[[8,11,8,14],[37,16,38,22],[50,16,51,22],[47,16,48,22],[44,3,45,4],[35,3,35,4],[37,3,37,4],[40,3,42,4],[40,5,45,13],[22,5,23,13],[29,3,29,13],[20,1,21,1],[20,16,23,22],[31,16,32,22],[25,3,25,4],[25,5,25,13],[25,16,25,22],[32,3,32,4],[27,3,27,4],[27,5,27,13],[27,16,27,22],[31,5,35,13],[20,3,20,4],[22,3,22,4],[31,3,31,4],[34,3,34,4],[20,5,20,13],[1,4,1,22],[37,5,38,13],[40,16,41,22],[4,16,4,22],[4,8,4,10],[4,2,4,5],[12,2,17,14],[10,2,10,14],[8,2,8,5],[8,8,8,10],[12,16,14,22],[6,2,6,5],[6,8,6,10],[6,16,6,22],[8,16,8,22],[10,16,10,22],[63,2,70,14],[63,16,68,22],[72,2,79,14],[72,16,74,22],[47,3,48,4],[50,3,51,4],[47,5,48,13],[50,5,51,13],[54,16,59,22],[54,2,61,14]]}
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[null,null,null,null,"SCENARIO SETUP\n© FAIR Institute. All rights reserved. "]
[]
[]
[null,"Analysis Created:",null,null,null,null,null,"Last Updated:",null,null,null,null,null,null,null,null,"Enter the date the analysis was created. Update with the last updated date."]
[]
[null,"Period Start:",null,null,null,null,null,"Period End:",null,null,null,null,null,null,null,null,"Provide the date range for which the scenario applies."]
[]
[null,"Currency:",null,null,null,null,null,"Symbol",{"$f":"IF(C9=\"Dollars (All Countries)\",\"$\",IF(C9=\"Euros\",\"€\",IF(C9=\"British Sterling Pounds\",\"£\",\"\")))","v":""},null,null,"[Symbol won't update until workbook is recalculated.]",null,null,null,null,"Choose the currency to be used for this scenario. No currecny translation will be performed."]
[]
[null,"Analysis Name:",null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Give your analysis a descriptive and unique name. Consider including a conbination of Asset, Threat Actor, Initial Attack Method, and Loss Outcome in the name."]
[]
[null,"Purpose:",null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Describe why you are performing this analysis for the business. What goal(s) do you hope to achieve? Why is the participate combination of Assets, Threat Actors, and Initial Attack Method being evaluated?"]
[]
[]
[]
[]
[]
[]
[]
[null,"Threat:",null,"Name:",null,null,null,null,null,null,null,null,null,null,null,null,"Name and describe the individuals, groups, or entities with the capability, opportunity, and motive to harm the organization’s assets. They can be external (e.g., nation-states, cybercriminals), internal (e.g., employees, contractors), or non-human (e.g., malware, natural disasters)."]
[]
[null,null,null,"Description:"]
[]
[]
[null,null,null,"Type:",null,null,null,null,null,null,null,null,null,null,null,null,"Choose one: Malicious, Accidental, Error, Failure, Natural"]
[]
[null,null,null,"Contact:",null,null,null,null,null,null,null,null,null,null,null,null,"Choose one: Random, Regular, Intentional"]
[]
[null,null,null,"If Type is Malicious, the following characteristics may be used."]
[]
[null,null,null,"Motive / intent:",null,null,null,null,null,null,null,null,null,null,null,null,"Examples: Profitseeking; ideology; nationalism; disruption of national infrastructure; create fear and uncertainty; steal cryptocurrency"]
[]
[]
[]
[]
[]
[null,null,null,"Sponsorship:",null,null,null,null,null,null,null,null,null,null,null,null,"Examples: State sponsored; self-funded via cybercrime"]
[]
[]
[null,null,null,"Preferred targets or target characteristics:",null,null,null,null,null,null,null,null,null,null,null,null,"Examples: Entities that are well-known to the nation's citizenry; organizations in the financial services sector; infrastructure assets; companies providing critical services for citizenry or businesses"]
[]
[]
[]
[]
[]
[]
[null,null,null,"Capability:",null,null,null,null,null,null,null,null,null,null,null,null,"Describe (qualitatively) the range of skills likely possessed by members of this threat actor or threat community."]
[]
[]
[null,null,null,"Personal risk tolerance:",null,null,null,null,null,null,null,null,null,null,null,null,"Describe the level of personal risk (e.g., civil fines; criminal fines; imprisonment; injury; death) the threat actors or community are likely be willing to take."]
[]
[]
[]
[null,"Asset:",null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Describe the asset in terms of its meaningful business value and potential for financial loss. Assets may be tangible (e.g., cash, facilities) or intangible (e.g., data, brand reputation, business processes), and their value often extends beyond what is reflected on a balance sheet. The description should specify the asset type—whether it is data, a business process, a product or service, cash and equivalents, or physical assets—along with its role in the organization."]
[]
[]
[]
[]
[]
[]
[]
[]
[null,"Method:",null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Describe the attack vector used to compromise or affect the asset. The method determines how the incident unfolds and influences the level of risk, potential impact, and the controls required to mitigate it. Methods range from ransomware attacks and data exfiltration to system outages and account takeovers, each carrying distinct operational, financial, and regulatory consequences. Clearly identifying the method ensures a more precise risk analysis and helps align mitigation strategies with the specific threats posed."]
[null,"(If Type is Malicious)"]
[]
[]
[]
[]
[]
[]
[]
[null,"Effect:",null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Describe the type of loss expected to result from an attack on a specific asset. The effect describes how the asset is impacted—whether its confidentiality, integrity, or availability is compromised—and how that impact translates into financial and operational losses for the business."]

{"sheet":"FAIR Model","index":3,"n_rows":55,"n_cols":98,"rows_kept":55,"cut":false,"hidden":true,"merged":[[2,44,2,57],[2,61,2,76],[3,15,7,40],[4,44,4,47],[4,49,4,52],[4,54,4,57],[4,61,5,101],[5,44,5,47],[5,49,5,52],[5,54,5,57],[9,80,9,85],[9,86,9,97],[10,80,10,85],[10,86,10,97],[17,5,17,21],[17,27,17,40],[17,68,17,81],[7,44,7,49],[7,50,7,56],[9,44,11,57],[9,61,9,66],[9,67,9,78],[19,78,19,81],[20,27,20,30],[20,32,20,35],[20,37,20,40],[20,68,20,71],[20,73,20,76],[20,78,20,81],[19,5,19,21],[19,27,19,30],[19,32,19,35],[19,37,19,40],[19,68,19,71],[19,73,19,76],[22,32,22,37],[22,73,22,78],[24,27,26,40],[24,68,26,81],[32,10,32,23],[32,44,32,57],[32,61,32,74],[32,78,32,91],[29,87,29,101],[34,61,34,64],[34,66,34,69],[34,71,34,74],[34,78,34,81],[34,83,34,86],[34,88,34,91],[34,10,34,13],[34,15,34,18],[34,20,34,23],[34,44,34,47],[34,49,34,52],[34,54,34,57],[35,61,35,64],[35,66,35,69],[35,71,35,74],[35,78,35,81],[35,83,35,86],[35,88,35,91],[35,10,35,13],[35,15,35,18],[35,20,35,23],[35,44,35,47],[35,49,35,52],[35,54,35,57],[47,2,47,15],[47,19,47,32],[47,36,47,49],[47,53,47,66],[47,70,47,83],[47,87,47,100],[37,15,37,20],[37,49,37,54],[37,66,37,71],[37,83,37,88],[39,10,41,23],[39,44,41,57],[39,61,41,74],[39,78,41,91],[49,87,49,90],[49,92,49,95],[49,97,49,100],[49,36,49,39],[49,41,49,44],[49,46,49,49],[49,53,49,56],[49,58,49,61],[49,63,49,66],[50,2,50,5],[50,7,50,10],[50,12,50,15],[50,19,50,22],[50,24,50,27],[50,29,50,32],[49,70,49,73],[49,75,49,78],[49,80,49,83],[49,2,49,5],[49,7,49,10],[49,12,49,15],[49,19,49,22],[49,24,49,27],[49,29,49,32],[50,70,50,73],[50,75,50,78],[50,80,50,83],[50,87,50,90],[50,92,50,95],[50,97,50,100],[50,36,50,39],[50,41,50,44],[50,46,50,49],[50,53,50,56],[50,58,50,61],[50,63,50,66],[54,2,56,15],[54,19,56,32],[54,36,56,49],[54,53,56,66],[54,70,56,83],[54,87,56,100],[52,7,52,12],[52,24,52,29],[52,41,52,46],[52,58,52,63],[52,75,52,80],[52,92,52,97]]}
[]
[]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Risk (Loss Exposure)",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Risk Analysis Name"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"FAIR MODEL\n© FAIR Institute. All rights reserved. "]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Minimum",null,null,null,null,"Average",null,null,null,null,"Maximum",null,null,null,null,null,null,{"$f":"IF('Scenario Setup'!C11=\"\",HYPERLINK(\"#'Analysis Scope'!C12\",\"<Enter name and dates on Analysis Setup sheet.>\"),'Scenario Setup'!C11)","v":"<Enter name and dates on Analysis Setup sheet.>"}]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF(RiskComplete,'Beta PERT & Monte Carlo'!CE14,\"\")","v":""},null,null,null,null,{"$f":"IF(RiskComplete,'Beta PERT & Monte Carlo'!CF14,\"\")","v":""},null,null,null,null,{"$f":"IF(RiskComplete,'Beta PERT & Monte Carlo'!CG14,\"\")","v":""}]
[]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Likelihood of Any Loss: ",null,null,null,null,null,{"$f":"IF(RiskComplete,1-(COUNTIF('Beta PERT & Monte Carlo'!CF21:CG10020,\"<.01\")/COUNT('Beta PERT & Monte Carlo'!CF21:CG10020)),\"\")","v":""}]
[]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF(LMComplete,IF(AB25<>\"\",\"Min, Average, Max values here are calculated using 10,000 Monte Carlo simulations of LEF and LM from below.\",\"\"),\"\")","v":""},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"'Scenario Setup'!B5","v":"Analysis Created:"},null,null,null,null,null,{"$f":"IF('Scenario Setup'!C5<>\"\",'Scenario Setup'!C5,\"\")","v":""},null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"'Scenario Setup'!H5","v":"Last Updated:"},null,null,null,null,null,{"$f":"IF('Scenario Setup'!I5<>\"\",'Scenario Setup'!I5,\"\")","v":""}]
[]
[]
[]
[]
[]
[]
[]
[null,null,null,null,null,"Choose the level at which you want to enter LEF factors:",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Loss Event Frequency (LEF)",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Loss Magnitude (LM)"]
[]
[null,null,null,null,null,"5. CF & PoA + TCap & RS (Lowest Level)",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Minimum",null,null,null,null,{"$f":"IF(LEFLevelLEF,\"Most Likely\",\"Average\")","v":"Average"},null,null,null,null,"Maximum",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Minimum",null,null,null,null,"Average",null,null,null,null,"Maximum"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF(LEFLevelLEF,\nIF(LEFComplete,'Beta PERT & Monte Carlo'!AE$6,\"\"),\nIF(Level2Complete+Level3Complete+Level4Complete+Level5Complete=1,'Beta PERT & Monte Carlo'!AE$13,\"\"))","v":""},null,null,null,null,{"$f":"IF(LEFLevelLEF,\nIF(LEFComplete,'Beta PERT & Monte Carlo'!AF$6,\"\"),\nIF(Level2Complete+Level3Complete+Level4Complete+Level5Complete=1,'Beta PERT & Monte Carlo'!AF$13,\"\"))","v":""},null,null,null,null,{"$f":"IF(LEFLevelLEF,\nIF(LEFComplete,'Beta PERT & Monte Carlo'!AG$6,\"\"),\nIF(Level2Complete+Level3Complete+Level4Complete+Level5Complete=1,'Beta PERT & Monte Carlo'!AG$13,\"\"))","v":""},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF(PLComplete+SLEFComplete+SLMCAdvLComplete=3,'Beta PERT & Monte Carlo'!CA14,\"\")","v":""},null,null,null,null,{"$f":"IF(PLComplete+SLEFComplete+SLMCAdvLComplete=3,'Beta PERT & Monte Carlo'!CB14,\"\")","v":""},null,null,null,null,{"$f":"IF(PLComplete+SLEFComplete+SLMCAdvLComplete=3,'Beta PERT & Monte Carlo'!CC14,\"\")","v":""}]
[]
[null,null,null,null,"RC","L1","L2","L3","L4","L5","CF","PoA","TEF","TCap","RS","Susc","LEF",null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Confidence:",{"$f":"IF(LEFLevelLEF,\nIF(LEFComplete,LEFConfidence,\"\"),\nIF(Level2Complete,\"N/A\",IF(Level3Complete,\"N/A\",IF(Level4Complete,\"N/A\",IF(Level5Complete,\"N/A\",\"\")))))","v":""},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Confidence:",{"$f":"IF(PLComplete+SLEFComplete+SLMCAdvLComplete=3,\"N/A\",\"\")","v":""}]
[]
[null,null,null,null,{"$f":"IF(LMComplete+Level1Complete+Level2Complete+Level3Complete+Level4Complete+Level5Complete=2,1,0)","v":0},{"$f":"IF(ISNUMBER(SEARCH(\"1\",LEFLevel,1)),IF(LEFComplete,1,0),0)","v":0},{"$f":"IF(ISNUMBER(SEARCH(\"2\",LEFLevel,1)),IF(TEFComplete+SuscComplete=2,1,0),0)","v":0},{"$f":"IF(ISNUMBER(SEARCH(\"3\",LEFLevel,1)),IF(TEFComplete+TCapComplete+RSComplete=3,1,0),0)","v":0},{"$f":"IF(ISNUMBER(SEARCH(\"4\",LEFLevel,1)),IF(CFComplete+PoAComplete+SuscComplete=3,1,0),0)","v":0},{"$f":"IF(ISNUMBER(SEARCH(\"5\",LEFLevel,1)),IF(CFComplete+PoAComplete+TCapComplete+RSComplete=4,1,0),0)","v":0},{"$f":"IF(ISNUMBER(SEARCH(\"CF\",LEFLevel,1)),1,0)","v":1},{"$f":"IF(ISNUMBER(SEARCH(\"PoA\",LEFLevel,1)),1,0)","v":1},{"$f":"IF(ISNUMBER(SEARCH(\"TEF\",LEFLevel,1)),1,0)","v":0},{"$f":"IF(ISNUMBER(SEARCH(\"TCap\",LEFLevel,1)),1,0)","v":1},{"$f":"IF(ISNUMBER(SEARCH(\"RS\",LEFLevel,1)),1,0)","v":1},{"$f":"IF(ISNUMBER(SEARCH(\"Susc\",LEFLevel,1)),1,0)","v":0},{"$f":"IF(ISNUMBER(SEARCH(\"LEF\",LEFLevel,1)),1,0)","v":0},null,null,null,null,null,null,null,null,null,null,{"$f":"IF(LEFLevelLEF,\nIF(LEFComplete,\"Min, Most Likely, Max and Confidence values here are provided by you and used in 10,000 Monte Carlo simulations to calculate LEF.\",\"LEF inputs are incomplete. Complete on the LEF sheet.\"),\nIF(Level2Complete,\"Min, Average, and Max above are based on 10,000 Monte Carlo simulations of TEF and Susc.\",IF(Level3Complete,\"Min, Average, and Max above are based on 10,000 Monte Carlo simulations of TEF and Susc.\",IF(Level4Complete,\"Min, Average, and Max above are based on 10,000 Monte Carlo simulations of TEF and Susc.\",IF(Level5Complete,\"Min, Average, and Max above are based on 10,000 Monte Carlo simulations of TEF and Susc.\",\"\")))))","v":""},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF(PLComplete+SLEFComplete+SLMCAdvLComplete=3,\"Min, Most Likely, Max and Confidence values here are calculated using the Monte Carlo Simulations of PL and SL from below.\",\"\")","v":""}]
[]
[]
[]
[]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Report Feedback to: FAIRU@FAIRInstitute.org"]
[]
[]
[null,null,null,null,null,null,null,null,null,null,"Threat Event Frequency (TEF)",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Susceptibility (Susc)",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Primary Loss (PL)",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Secondary Loss (SL)"]
[]
[null,null,null,null,null,null,null,null,null,null,"Minimum",null,null,null,null,{"$f":"IF(LEFLevelTEF,\"Most Likely\",\"Average\")","v":"Average"},null,null,null,null,"Maximum",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Minimum",null,null,null,null,{"$f":"IF(LEFLevelSusc,\"Most Likely\",\"Average\")","v":"Average"},null,null,null,null,"Maximum",null,null,null,null,null,null,"Minimum",null,null,null,null,"Most Likely",null,null,null,null,"Maximum",null,null,null,null,null,null,"Minimum",null,null,null,null,"Average",null,null,null,null,"Maximum"]
[null,null,null,null,null,null,null,null,null,null,{"$f":"IF(LEFLevelTEF,IF(TEFComplete,'Beta PERT & Monte Carlo'!O$6,\"\"),\nIF(LEFLevelCF,\nIF(CFComplete+PoAComplete=2,'Beta PERT & Monte Carlo'!O$13,\"\"),\"\"))","v":""},null,null,null,null,{"$f":"IF(LEFLevelTEF,IF(TEFComplete,'Beta PERT & Monte Carlo'!P$6,\"\"),\nIF(LEFLevelCF,\nIF(CFComplete+PoAComplete=2,'Beta PERT & Monte Carlo'!P$13,\"\"),\"\"))","v":""},null,null,null,null,{"$f":"IF(LEFLevelTEF,IF(TEFComplete,'Beta PERT & Monte Carlo'!Q$6,\"\"),\nIF(LEFLevelCF,\nIF(CFComplete+PoAComplete=2,'Beta PERT & Monte Carlo'!Q$13,\"\"),\"\"))","v":""},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF(LEFLevelSusc,IF(SuscComplete,'Beta PERT & Monte Carlo'!AA6,\"\"),\nIF(LEFLevelRS,\nIF(TCapComplete+RSComplete=2,'Beta PERT & Monte Carlo'!AA13,\"\"),\"\"))","v":""},null,null,null,null,{"$f":"IF(LEFLevelSusc,IF(SuscComplete,'Beta PERT & Monte Carlo'!AB6,\"\"),\nIF(LEFLevelRS,\nIF(TCapComplete+RSComplete=2,'Beta PERT & Monte Carlo'!AB13,\"\"),\"\"))","v":""},null,null,null,null,{"$f":"IF(LEFLevelSusc,IF(SuscComplete,'Beta PERT & Monte Carlo'!AC6,\"\"),\nIF(LEFLevelRS,\nIF(TCapComplete+RSComplete=2,'Beta PERT & Monte Carlo'!AC13,\"\"),\"\"))","v":""},null,null,null,null,null,null,{"$f":"'Beta PERT & Monte Carlo'!BK7","v":""},null,null,null,null,{"$f":"'Beta PERT & Monte Carlo'!BL7","v":""},null,null,null,null,{"$f":"'Beta PERT & Monte Carlo'!BM7","v":""},null,null,null,null,null,null,{"$f":"IF(SLEFComplete+SLMCAdvLComplete=2,'Beta PERT & Monte Carlo'!BG14,\"\")","v":""},null,null,null,null,{"$f":"IF(SLEFComplete+SLMCAdvLComplete=2,'Beta PERT & Monte Carlo'!BH14,\"\")","v":""},null,null,null,null,{"$f":"IF(SLEFComplete+SLMCAdvLComplete=2,'Beta PERT & Monte Carlo'!BI14,\"\")","v":""}]
[]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Confidence:",{"$f":"IF(LEFLevelTEF,IF(TEFComplete,TEF!D15,\"\"),IF(CFComplete+PoAComplete=2,\"N/A\",\"\"))","v":""},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Confidence:",{"$f":"IF(LEFLevelSusc,IF(SuscComplete,Susc!D15,\"\"),IF(TCapComplete+RSComplete=2,\"N/A\",\"\"))","v":""},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Confidence:",{"$f":"PLConfidence","v":""},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Confidence:",{"$f":"IF(SLEFComplete+SLMCAdvLComplete=2,\"N/A\",\"\")","v":""}]
[]
[null,null,null,null,null,null,null,null,null,null,{"$f":"IF(LEFLevelTEF,\nIF(TEFComplete,\"Min, Most Likely, Max and Confidence values here are provided by you and used in 10,000 Monte Carlo simulations to calculate TEF.\",\"TEF inputs are incomplete. Complete on the TEF sheet.\"),\nIF(LEFLevelCF,IF(CFComplete+PoAComplete=2,\"Min, Average, and Max above are based on 10,000 Monte Carlo simulutions oof CF and PoA.\",\"\"),\"\"))","v":""},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF(LEFLevelSusc,\nIF(SuscComplete,\"Min, Most Likely, Max and Confidence values here are provided by you and used in 10,000 Monte Carlo simulations to calculate Susc.\",\"Susc inputs are incomplete. Complete on the Susc sheet.\"),\nIF(LEFLevelRS,IF(TCapComplete+RSComplete=2,\"Min, Average, and Max above are based on 10,000 Monte Carlo simulutions oof TCap and RS.\",\"\"),\"\"))","v":""},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF(PLComplete,\"Min, Most Likely, Max and Confidence values here are entered by you and used in a Monte Carlo simulation to calculate PL.\",\"PL inputs are incomplete. Complete on the PL sheet.\")","v":"PL inputs are incomplete. Complete on the PL sheet."},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF(PLComplete+SLEFComplete+SLMCAdvLComplete=3,\"Min, Most Likely, Max and Confidence values here are calculated using the Monte Carlo Simulations of SLEF and SLM from below.\",\"\")","v":""}]
[]
[]
[]
[]
[]
[]
[]
[null,null,"Contact Frequency (CF)",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Probability of Action (PoA)",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Threat Capability (TCap)",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Resistance Strength (RS)",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Secondary Loss Event Frequency (SLEF)",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Secondary Loss Magnitude (SLM)"]
[]
[null,null,"Minimum",null,null,null,null,"Most Likely",null,null,null,null,"Maximum",null,null,null,null,null,null,"Minimum",null,null,null,null,"Most Likely",null,null,null,null,"Maximum",null,null,null,null,null,null,"Minimum",null,null,null,null,"Most Likely",null,null,null,null,"Maximum",null,null,null,null,null,null,"Minimum",null,null,null,null,"Most Likely",null,null,null,null,"Maximum",null,null,null,null,null,null,"Minimum",null,null,null,null,"Most Likely",null,null,null,null,"Maximum",null,null,null,null,null,null,"Minimum",null,null,null,null,"Most Likely",null,null,null,null,"Maximum"]
[null,null,{"$f":"IF(LEFLevelCF,'Beta PERT & Monte Carlo'!G6,\"\")","v":""},null,null,null,null,{"$f":"IF(LEFLevelCF,'Beta PERT & Monte Carlo'!H6,\"\")","v":""},null,null,null,null,{"$f":"IF(LEFLevelCF,'Beta PERT & Monte Carlo'!I6,\"\")","v":""},null,null,null,null,null,null,{"$f":"IF(LEFLevelCF,'Beta PERT & Monte Carlo'!K6,\"\")","v":""},null,null,null,null,{"$f":"IF(LEFLevelCF,'Beta PERT & Monte Carlo'!L6,\"\")","v":""},null,null,null,null,{"$f":"IF(LEFLevelCF,'Beta PERT & Monte Carlo'!M6,\"\")","v":""},null,null,null,null,null,null,{"$f":"IF(LEFLevelTCap,'Beta PERT & Monte Carlo'!S6,\"\")","v":""},null,null,null,null,{"$f":"IF(LEFLevelTCap,'Beta PERT & Monte Carlo'!T6,\"\")","v":""},null,null,null,null,{"$f":"IF(LEFLevelTCap,'Beta PERT & Monte Carlo'!U6,\"\")","v":""},null,null,null,null,null,null,{"$f":"IF(LEFLevelRS,'Beta PERT & Monte Carlo'!W6,\"\")","v":""},null,null,null,null,{"$f":"IF(LEFLevelRS,'Beta PERT & Monte Carlo'!X6,\"\")","v":""},null,null,null,null,{"$f":"IF(LEFLevelRS,'Beta PERT & Monte Carlo'!Y6,\"\")","v":""},null,null,null,null,null,null,{"$f":"'Beta PERT & Monte Carlo'!AI7","v":""},null,null,null,null,{"$f":"'Beta PERT & Monte Carlo'!AJ7","v":""},null,null,null,null,{"$f":"'Beta PERT & Monte Carlo'!AK7","v":""},null,null,null,null,null,null,{"$f":"'Beta PERT & Monte Carlo'!AM7","v":""},null,null,null,null,{"$f":"'Beta PERT & Monte Carlo'!AN7","v":""},null,null,null,null,{"$f":"'Beta PERT & Monte Carlo'!AO7","v":""}]
[]
[null,null,null,null,null,null,"Confidence:",{"$f":"IF(LEFLevelCF,IF(CFComplete,CF!D15,\"\"),\"\")","v":""},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Confidence:",{"$f":"IF(LEFLevelPoA,IF(PoAComplete,PoAConfidence,\"\"),\"\")","v":""},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Confidence:",{"$f":"IF(LEFLevelTCap,IF(TCapComplete,TCap!D15,\"\"),\"\")","v":""},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Confidence:",{"$f":"IF(LEFLevelRS,IF(RSComplete,RS!D15,\"\"),\"\")","v":""},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Confidence:",{"$f":"IF(SLEFComplete,SLEF!D15,\"\")","v":""},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Confidence:",{"$f":"SLMConfidence","v":0}]
[]
[null,null,{"$f":"IF(LEFLevelCF,IF(CFComplete,\"Min, Most Likely, Max and Confidence values here are provided by you and used in 10,000 Monte Carlo simulations to calculate CF.\",\"CF inputs are incomplete. Complete on the CF sheet.\"),\"\")","v":"CF inputs are incomplete. Complete on the CF sheet."},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF(LEFLevelPoA,IF(PoAComplete,\"Min, Most Likely, Max and Confidence values here are provided by you and used in 10,000 Monte Carlo simulations to calculate PoA.\",\"PoA inputs are incomplete. Complete on the PoA sheet.\"),\"\")","v":"PoA inputs are incomplete. Complete on the PoA sheet."},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF(LEFLevelTCap,IF(TCapComplete,\"Min, Most Likely, Max and Confidence values here are provided by you and used in 10,000 Monte Carlo simulations to calculate TCap.\",\"TCap inputs are incomplete. Complete on the TCap sheet.\"),\"\")","v":"TCap inputs are incomplete. Complete on the TCap sheet."},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF(LEFLevelRS,IF(RSComplete,\"Min, Most Likely, Max and Confidence values here are provided by you and used in 10,000 Monte Carlo simulations to calculate RS.\",\"RS inputs are incomplete. Complete on the RS sheet.\"),\"\")","v":"RS inputs are incomplete. Complete on the RS sheet."},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF(SLEFComplete,\"Min, Most Likely, Max and Confidence values here are entered by you and used in a Monte Carlo simulation to calculate SLEF.\",\"SLEFinputs are incomplete. Complete on the SLEF sheet.\")","v":"SLEFinputs are incomplete. Complete on the SLEF sheet."},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF(SLMComplete,\"Min, Most Likely, Max and Confidence values here are entered by you and used in a Monte Carlo simulation to calculate SLM.\",\"SLM inputs are incomplete. Complete on the SLM sheet.\")","v":"SLM inputs are incomplete. Complete on the SLM sheet."}]

{"sheet":"LEF","index":4,"n_rows":17,"n_cols":15,"rows_kept":17,"cut":false,"hidden":true,"merged":[[1,4,1,15],[4,2,4,12],[4,14,29,14],[6,2,6,12],[8,5,8,12],[10,5,10,12],[12,5,12,12],[14,5,14,12],[18,2,29,12]]}
[]
[null,null,null,null,"LOSS EVENT FREQUENCY INPUT\n© FAIR Institute. All rights reserved. "]
[]
[]
[null,null,"Loss Event Frequency (LEF)",null,null,null,null,null,null,null,null,null,null,null,"Guidance:\nWhen estimating Loss Event Frequency (LEF) in the FAIR model without directly calculating Threat Event Frequency (TEF) and Susceptibility, take a top-down approach using historical loss data, industry benchmarks, and expert judgment. This involves reviewing past security incidents within the organization and analyzing how often similar organizations in the same sector experience loss events. Reports such as the Verizon DBIR, ISAC threat intelligence, and regulatory breach disclosures (e.g., GDPR, SEC filings) provide valuable reference points.\n\nYou should also consider your organization’s overall security posture and industry trends to infer a reasonable LEF estimate. For example, if healthcare organizations frequently experience ransomware-related disruptions, a healthcare entity may assume a moderate-to-high LEF even without precise TEF or Susceptibility calculations. Additionally, analysts can conduct scenario modeling, where multiple plausible LEF estimates are developed based on varying levels of security posture and threat activity.\n\nConsider the use of a commercial cyber risk management system to integrate data from threat monitoring services, vulnerability assessments, configuration management tools, audits or other sources to calculate LEF (usually by updating and calculating the underlying factors of the FAIR Model). Such services and tools can help reduce the burden of LEF estimations and provide regular or continuous LEF updates."]
[]
[null,null,"How many times over the next year is the loss event likely to occur?\n"]
[]
[null,null,"Minimum:",null,null,{"$f":"IF(LEFLevelLEF,\"Integer value. Must be less than or equal to Most Likely value.\",HYPERLINK(\"#'FAIR Model'!F20\",CONCATENATE(\"You've chosen to enter LEF factors @ \",RIGHT('FAIR Model'!F$20,LEN('FAIR Model'!F$20)-3),\". Use those input sheets instead.\")))","v":"You've chosen to enter LEF factors @ CF & PoA + TCap & RS (Lowest Level). Use those input sheets instead."}]
[]
[null,null,"Most Likely:",null,null,{"$f":"IF(LEFLevelLEF,\"Integer value. Must be greater than or equal to Minimum value AND less than or equal to Maximum value.\",\"\")","v":""}]
[]
[null,null,"Maximum:",null,null,{"$f":"IF(LEFLevelLEF,\"Integer value. Must be greater than or equal to Most Likely value.\",\"\")","v":""}]
[]
[null,null,"Confidence:",null,null,{"$f":"IF(LEFLevelLEF,\"Describes your level of confidence in the estimate. Modifies the resulting curve around the Most Likely.\",\"\")","v":""}]
[null,null,null,{"$f":"(IF(COUNTIF(D9:D15,\"\")=3,1,0))","v":0}]
[null,null,"Document your rationale for the values you've chosen. (Optional)"]

{"sheet":"Susc","index":5,"n_rows":17,"n_cols":15,"rows_kept":17,"cut":false,"hidden":true,"merged":[[1,4,1,15],[4,2,4,12],[4,14,29,14],[6,2,6,12],[8,5,8,12],[10,5,10,12],[12,5,12,12],[14,5,14,12],[18,2,29,12]]}
[]
[null,null,null,null,"SUSCEPTIBILITY INPUT\n© FAIR Institute. All rights reserved. "]
[]
[]
[null,null,"Susceptibility (Susc)",null,null,null,null,null,null,null,null,null,null,null,"Guidance:\nWhen estimating Susceptibility (Susc) using the FAIR model without directly analyzing the lower-level factors of Threat Capability (TCap) and Resistance Strength (RS), use a higher-level, holistic approach based on historical data, industry trends, and expert judgment. Susceptibility represents the likelihood that a threat actor’s efforts to compromise an asset will succeed, given the organization’s defenses and the actor’s capabilities.\n\nTo estimate Susc at a high level, review historical incident data from within the organization or across the industry to identify the success rates of similar threats against comparable defenses. For example, if ransomware campaigns frequently bypass controls in the industry, it suggests a higher Susceptibility to those threats. Also consider your organization’s security posture as a whole, such as the maturity of its cybersecurity program, the comprehensiveness of its controls, and known gaps in its defenses (e.g., unpatched systems or misconfigurations). Where possible, incorporate assessments such as red team exercises, penetration test findings, or third-party risk assessments. \n\nConsider the use of a commercial cyber risk management system to integrate data from threat monitoring services, vulnerability assessments, configuration management tools, audits or other sources to calculate Susc (usually by calculating TCap and RS directly). Such services and tools can help reduce the burden of Susc estimations and provide regular or continuous Susc updates often via TCap and RS directly."]
[]
[null,null,"What percentage of threat events are likely to result in loss events?"]
[]
[null,null,"Minimum:",null,null,{"$f":"IF(LEFLevelSusc,\"Percentage between 0% and 100%. Must be less than or equal to Most Likely value.\",HYPERLINK(\"#'FAIR Model'!F20\",CONCATENATE(\"You've chosen to enter LEF factors @ \",RIGHT('FAIR Model'!F$20,LEN('FAIR Model'!F$20)-3),\". Use those input sheets instead.\")))","v":"You've chosen to enter LEF factors @ CF & PoA + TCap & RS (Lowest Level). Use those input sheets instead."}]
[]
[null,null,"Most Likely:",null,null,{"$f":"IF(LEFLevelSusc,\"Percentage between 0% and 100%. Must be less than or equal to Maximum value.\",\"\")","v":""}]
[]
[null,null,"Maximum:",null,null,{"$f":"IF(LEFLevelSusc,\"Percentage between 0% and 100%. Must be greater than or equal to Most Likely value.\",\"\")","v":""}]
[]
[null,null,"Confidence:",null,null,{"$f":"IF(LEFLevelSusc,\"Describes your level of confidence in the estimate. Modifies the resulting curve around the Most Likely.\",\"\")","v":""}]
[null,null,null,{"$f":"(IF(COUNTIF(D9:D15,\"\")=3,1,0))","v":0}]
[null,null,"Document your rationale for the values you've chosen. (Optional)"]

{"sheet":"TCap","index":6,"n_rows":33,"n_cols":15,"rows_kept":33,"cut":false,"hidden":true,"merged":[[1,4,1,15],[32,1,62,5],[4,2,4,12],[4,14,29,14],[6,2,6,12],[8,5,8,12],[10,5,10,12],[12,5,12,12],[14,5,14,12],[18,2,29,12]]}
[]
[null,null,null,null,"THREAT CAPABILITY INPUT\n© FAIR Institute. All rights reserved. "]
[]
[]
[null,null,"Threat Capability (TCap)",null,null,null,null,null,null,null,null,null,null,null,"Guidance:\nDetermining the capability level of threat actors (TCap) involves assessing their technical skills, resources, and expertise required to execute an attack successfully. Start by profiling the threat actor categories (e.g., nation-states, cybercriminals, or insiders) and analyzing their historical behaviors, known tactics, techniques, and procedures (TTPs). Leverage threat intelligence feeds, frameworks like MITRE ATT&CK, and incident reports to help identify the sophistication and persistence levels of specific actors targeting your industry or organization.\n\nKey factors to evaluate include the threat actor’s access to advanced tools (e.g., zero-day exploits, malware kits), financial or organizational resources, and their ability to adapt to defensive measures. For example, nation-states often demonstrate high capability with tailored exploits and extensive operational funding, while opportunistic cybercriminals may rely on commoditized tools and scripts. By combining these insights with an understanding of your organization’s attack surface and defensive posture, you can estimate whether the actor’s capability is sufficient to bypass existing controls and exploit vulnerabilities.\n\nFAIR emphasizes quantifying the threat capability level as part of broader risk modeling. Assign a value from 0% (no capability) to 100% (absolute capability) to the actor’s ability to overcome controls and achieve their objectives. Service providers or a commercial cyber risk management system may also provide TCap estimates based on the threat actor or threat community. Such services and tools can help reduce the burden of TCap estimations and provide regular or continuous TCap updates."]
[]
[null,null,"How capable is this threat community of successfully carrying out the threat event when compared to the Capability Continuum?"]
[]
[null,null,"Minimum:",null,null,{"$f":"IF(LEFLevelTCap,\"Percentage between 0% and 100%. Must be less than or equal to Most Likely value.\",HYPERLINK(\"#'FAIR Model'!F20\",CONCATENATE(\"You've chosen to enter LEF factors @ \",RIGHT('FAIR Model'!F$20,LEN('FAIR Model'!F$20)-3),\". Use those input sheets instead.\")))","v":"Percentage between 0% and 100%. Must be less than or equal to Most Likely value."}]
[]
[null,null,"Most Likely:",null,null,{"$f":"IF(LEFLevelTCap,\"Percentage between 0% and 100%. Must be less than or equal to Maximum value.\",\"\")","v":"Percentage between 0% and 100%. Must be less than or equal to Maximum value."}]
[]
[null,null,"Maximum:",null,null,{"$f":"IF(LEFLevelTCap,\"Percentage between 0% and 100%. Must be greater than or equal to Most Likely value.\",\"\")","v":"Percentage between 0% and 100%. Must be greater than or equal to Most Likely value."}]
[]
[null,null,"Confidence:",null,null,{"$f":"IF(LEFLevelTCap,\"Describes your level of confidence in the estimate. Modifies the resulting curve around the Most Likely.\",\"\")","v":"Describes your level of confidence in the estimate. Modifies the resulting curve around the Most Likely."}]
[null,null,null,{"$f":"(IF(COUNTIF(D9:D15,\"\")=3,1,0))","v":0}]
[null,null,"Document your rationale for the values you've chosen. (Optional)"]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[null,"IMPORTANT:\nThe TCap histogram chart at the right is based on running 10,000 Monte Carlo simulations. Those simulations are only used to population the chart. \n\nThen, the Susc simulations use a combination of TCap and RS random values (based on their Beta PERT distributions) together in a single calculation (Excel formula). This calculation runs an array of iterations equal to the value of TEF for the Monte Carlo simulation. For example, if TEF for a single simulation (out of 10,000) has a value of six (6), the Susc calculation will perform six (6) iterations within that one simulation. Those six (6) iterations each calculate TCap and RS using the Beta PERT distributions. In that way, each simulated Threat Event includes its own determination of success or failure. \n\nAs a result of this model, this workbook will perform 10,000 times the array of TEF values. If the average TEF value is, say, 4.5, the workbook will perform 45,000 simulations of TCap and RS when calculating Susc."]

{"sheet":"RS","index":7,"n_rows":33,"n_cols":15,"rows_kept":33,"cut":false,"hidden":true,"merged":[[1,4,1,15],[32,1,62,5],[4,2,4,12],[4,14,29,14],[6,2,6,12],[8,5,8,12],[10,5,10,12],[12,5,12,12],[14,5,14,12],[18,2,29,12]]}
[]
[null,null,null,null,"RESISTANCE STRENGTH INPUT\n© FAIR Institute. All rights reserved. "]
[]
[]
[null,null,"Resistance Strength (RS)",null,null,null,null,null,null,null,null,null,null,null,"Guidance:\nIn FAIR, resistance strength (RS) refers to the ability of resistive controls to prevent or withstand a threat actor’s attempts to successfully exploit a vulnerability. To determine resistance strength, evaluate the effectiveness and maturity of existing controls in mitigating specific attack vectors. \n\nPer FAIR-CAM, resistive controls seek to keep threat events from becoming loss events. They include all the measures put in place to mitigate vulnerabilities and bolster defenses, for example employee training, patching, authentication, access management.\n\nTo quantify resistance strength, provide an estimated value (for minimum, most likely, and maximum) of the likelihood that resistive controls will successfully impede an attack via the expected method(s) used by the threat actor/community. Assign RS a numerical range (from 0% to 100%) based on empirical data and testing results about your controls. 0% means a control will NOT impede an attack; 100% means a control will ALWAYS impede an attack. \n\nNote that in our Monte Carlo simulations, when TCap > RS, then an attack is considered successful. When TCap <= RS, then the attack is considered unsuccessful. Depending on TCap and RS values, the simulution will result in some iterations being successful and others being unsuccessful.\n\nConsider the use of a commercial cyber risk management system to integrate data from vulnerability assessments, configuration management tools, audits or other sources to calculate RS values. Such services and tools can help reduce the burden of RS estimations and provide regular or continuous RS updates."]
[]
[null,null,"Given our controls, what is the highest percentile of the Capability Continuum we think we will successfully defeat when they take a threat action?"]
[]
[null,null,"Minimum:",null,null,{"$f":"IF(LEFLevelRS,\"Percentage between 0% and 100%. Must be less than or equal to Most Likely value.\",HYPERLINK(\"#'FAIR Model'!F20\",CONCATENATE(\"You've chosen to enter LEF factors @ \",RIGHT('FAIR Model'!F$20,LEN('FAIR Model'!F$20)-3),\". Use those input sheets instead.\")))","v":"Percentage between 0% and 100%. Must be less than or equal to Most Likely value."}]
[]
[null,null,"Most Likely:",null,null,{"$f":"IF(LEFLevelRS,\"Percentage between 0% and 100%. Must be less than or equal to Maximum value.\",\"\")","v":"Percentage between 0% and 100%. Must be less than or equal to Maximum value."}]
[]
[null,null,"Maximum:",null,null,{"$f":"IF(LEFLevelRS,\"Percentage between 0% and 100%. Must be greater than or equal to Most Likely value.\",\"\")","v":"Percentage between 0% and 100%. Must be greater than or equal to Most Likely value."}]
[]
[null,null,"Confidence:",null,null,{"$f":"IF(LEFLevelRS,\"Describes your level of confidence in the estimate. Modifies the resulting curve around the Most Likely.\",\"\")","v":"Describes your level of confidence in the estimate. Modifies the resulting curve around the Most Likely."}]
[null,null,null,{"$f":"(IF(COUNTIF(D9:D15,\"\")=3,1,0))","v":0}]
[null,null,"Document your rationale for the values you've chosen. (Optional)"]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[null,"IMPORTANT:\nThe RS histogram chart at the right is based on running 10,000 Monte Carlo simulations. Those simulations are only used to population the chart. \n\nThen, the Susc simulations use a combination of TCap and RS random values (based on their Beta PERT distrubutions) together in a single calculation (Excel formula). This calculation runs an array of iterations equal to the value of TEF for the Monte Carlo simulation. For example, if TEF for a single simulation (out of 10,000) has a value of six (6), the Susc calculation will perform six (6) iterations within that one simulation. Those six (6) iterations each cacluation TCap and RS using the Beta PERT distributions. In that way, each simulated Threat Event includes its own determination of success or failure. \n\nAs a result of this model, this workbook will perform 10,000 times the array of TEF values. If the average TEF value is, say, 4.5, the workbook will perform 45,000 simulations of TCap and RS when calculating Susc."]

{"sheet":"TEF","index":8,"n_rows":17,"n_cols":15,"rows_kept":17,"cut":false,"hidden":true,"merged":[[1,4,1,15],[1,1,1,3],[4,2,4,12],[4,14,29,14],[6,2,6,12],[8,5,8,12],[10,5,10,12],[12,5,12,12],[14,5,14,12],[18,2,29,12]]}
[]
[null,null,null,null,"THREAT EVENT FREQUENCY INPUT\n© FAIR Institute. All rights reserved. "]
[]
[]
[null,null,"Threat Event Frequency (TEF)",null,null,null,null,null,null,null,null,null,null,null,"Guidance:\nWhen estimating Threat Event Frequency (TEF) using the FAIR model without directly estimating Contact Frequency (CF) and Probability of Action (PoA), take a more top-down approach by relying on aggregated data, expert judgment, and industry trends. Evaluate historical threat activity targeting similar organizations, sectors, or geographies to determine how often threat events are likely to occur. Sources such as internal incident logs, threat intelligence reports, and industry benchmarks (e.g., Verizon DBIR, ISAC reports) provide valuable data for estimating TEF at a high level.\n\nConsider external factors like your organization’s attractiveness to specific threat actors, your industry’s threat landscape, and trends in threat activity (e.g., seasonal ransomware spikes or geopolitical events). For example, if your sector has been heavily targeted by ransomware campaigns over the past year, you might estimate a higher TEF for cybercriminal activity. Threat intelligence platforms and frameworks like MITRE ATT&CK can further help identify the frequency of tactics used by relevant threat groups.\n\nConsider using service providers or a commercial cyber risk management system to provide TEF estimates based on the threat actor, attack methods, industry, geography, and other factors. Such services and tools can help reduce the burden of TEF estimations and provide regular or continuous TEF updates."]
[]
[null,null,"How many times over the next year is the threat event likely to occur? How many times will the asset face a threat action?\n"]
[]
[null,null,"Minimum:",null,null,{"$f":"IF(LEFLevelTEF,\"Integer value. Must be less than or equal to Most Likely value.\",HYPERLINK(\"#'FAIR Model'!F20\",CONCATENATE(\"You've chosen to enter LEF factors @ \",RIGHT('FAIR Model'!F$20,LEN('FAIR Model'!F$20)-3),\". Use those input sheets instead.\")))","v":"You've chosen to enter LEF factors @ CF & PoA + TCap & RS (Lowest Level). Use those input sheets instead."}]
[]
[null,null,"Most Likely:",null,null,{"$f":"IF(LEFLevelTEF,\"Integer value. Must be greater than or equal to Minimum value AND less than or equal to Maximum value.\",\"\")","v":""}]
[]
[null,null,"Maximum:",null,null,{"$f":"IF(LEFLevelTEF,\"Integer value. Must be greater than or equal to Most Likely value.\",\"\")","v":""}]
[]
[null,null,"Confidence:",null,null,{"$f":"IF(LEFLevelTEF,\"Describes your level of confidence in the estimate. Modifies the resulting curve around the Most Likely.\",\"\")","v":""}]
[null,null,null,{"$f":"(IF(COUNTIF(D9:D15,\"\")=3,1,0))","v":0}]
[null,null,"Document your rationale for the values you've chosen. (Optional)"]

{"sheet":"CF","index":9,"n_rows":17,"n_cols":15,"rows_kept":17,"cut":false,"hidden":true,"merged":[[1,4,1,15],[12,5,12,12],[4,14,29,14],[14,5,14,12],[4,2,4,12],[6,2,6,12],[18,2,29,12],[10,5,10,12],[8,5,8,12]]}
[]
[null,null,null,null,"CONTACT FREQUENCY INPUT\n© FAIR Institute. All rights reserved. "]
[]
[]
[null,null,"Contract Frequency (CF)",null,null,null,null,null,null,null,null,null,null,null,"Guidance:\nTo estimate the Contact Frequency (CF) of expected threats, you should analyze historical data, industry trends, and real-time threat intelligence. Start by profiling threats based on categories like nation-states, cybercriminals, or APTs, and assess their relevance to your industry and geography. Leverage external sources (e.g., MITRE ATT&CK, Verizon DBIR) and internal incident data to gauge how often threats have targeted similar organizations. Consider seasonal patterns, known threat campaigns, and ongoing activity by specific actors to refine frequency estimates.\n\nContextual factors, such as geopolitical events, economic trends, and industry-specific risks, play a critical role in understanding the likelihood of future threat activity. Continuous monitoring of threat intelligence feeds, along with periodic analysis of internal logs and reports, will help you stay updated on the evolving threat landscape. By incorporating these data sources and patterns, you can build a clearer picture of how frequently specific types of threats are likely to emerge.\n\nConsider the impact of existing controls on reducing CF. Per FAIR-CAM, avoidance controls reduce CF. These include things such as firewalls and perimeter security which limit exposure of an asset. Then consider the effects of Variance Management Controls and Decision Support Controls on your avoidance controls and, hence, CF. \n\nConsider using service providers or a commercial cyber risk management system to provide CF estimates based on the threat actor, attack methods, industry, geography, and other factors. Such services and tools can help reduce the burden of CF estimations and provide regular or continuous CF updates."]
[]
[null,null,"How many times over the next year is the threat actor/agent likely to reach the asset?"]
[]
[null,null,"Minimum:",null,null,{"$f":"IF(LEFLevelCF,\"Integer value. Must be less than or equal to Most Likely value.\",HYPERLINK(\"#'FAIR Model'!F20\",CONCATENATE(\"You've chosen to enter LEF factors @ \",RIGHT('FAIR Model'!F$20,LEN('FAIR Model'!F$20)-3),\". Use those input sheets instead.\")))","v":"Integer value. Must be less than or equal to Most Likely value."}]
[]
[null,null,"Most Likely:",null,null,{"$f":"IF(LEFLevelCF,\"Integer value. Must be greater than or equal to Minimum value AND less than or equal to Maximum value.\",\"\")","v":"Integer value. Must be greater than or equal to Minimum value AND less than or equal to Maximum value."}]
[]
[null,null,"Maximum:",null,null,{"$f":"IF(LEFLevelCF,\"Integer value. Must be greater than or equal to Most Likely value.\",\"\")","v":"Integer value. Must be greater than or equal to Most Likely value."}]
[]
[null,null,"Confidence:",null,null,{"$f":"IF(LEFLevelCF,\"Describes your level of confidence in the estimate. Modifies the resulting curve around the Most Likely.\",\"\")","v":"Describes your level of confidence in the estimate. Modifies the resulting curve around the Most Likely."}]
[null,null,null,{"$f":"(IF(COUNTIF(D9:D15,\"\")=3,1,0))","v":0}]
[null,null,"Document your rationale for the values you've chosen. (Optional)"]

{"sheet":"PoA","index":10,"n_rows":17,"n_cols":15,"rows_kept":17,"cut":false,"hidden":true,"merged":[[1,4,1,15],[4,2,4,12],[4,14,29,14],[6,2,6,12],[8,5,8,12],[10,5,10,12],[12,5,12,12],[14,5,14,12],[18,2,29,12]]}
[]
[null,null,null,null,"PROBABILITY OF ACTION INPUT\n© FAIR Institute. All rights reserved. "]
[]
[]
[null,null,"Probability of Action (PoA)",null,null,null,null,null,null,null,null,null,null,null,"Guidance:\nProbability of Action depends on the value of the asset, the level of effort, and the risk of detection/capture. To estimate the Probability of Action (PoA) for a given threat actor, analyze the value of the asset, the level of effort the threat actor must sustain, and the risk of detection/capture.\n\nConsider the impact of existing controls on reducing PoA. Per FAIR-CAM, deterrence controls reduce PoA. These include, for example, visible cameras, legal/criminal notices, acceptable use policies, advertisements of monitoring, encryption or obfuscation of an asset's value. \n\nConsider using service providers or a commercial cyber risk management system to provide PoA estimates based on the threat actor, attack methods, industry, geography, and other factors. They may also estimate the impact of Loss Event Controls that dissuade threats. Such services and tools can help reduce the burden of PoA estimations and provide regular or continuous PoA updates."]
[]
[null,null,"What percentage of threat agent/actor contacts with the asset are likely to result in threat events? \nHow often will the threat decide to act against the asset?"]
[]
[null,null,"Minimum:",null,null,{"$f":"IF(LEFLevelPoA,\"Percentage between 0% and 100%. Must be less than or equal to Most Likely value.\",HYPERLINK(\"#'FAIR Model'!F20\",CONCATENATE(\"You've chosen to enter LEF factors @ \",RIGHT('FAIR Model'!F$20,LEN('FAIR Model'!F$20)-3),\". Use those input sheets instead.\")))","v":"Percentage between 0% and 100%. Must be less than or equal to Most Likely value."}]
[]
[null,null,"Most Likely:",null,null,{"$f":"IF(LEFLevelPoA,\"Percentage between 0% and 100%. Must be less than or equal to Maximum value.\",\"\")","v":"Percentage between 0% and 100%. Must be less than or equal to Maximum value."}]
[]
[null,null,"Maximum:",null,null,{"$f":"IF(LEFLevelPoA,\"Percentage between 0% and 100%. Must be greater than or equal to Most Likely value.\",\"\")","v":"Percentage between 0% and 100%. Must be greater than or equal to Most Likely value."}]
[]
[null,null,"Confidence:",null,null,{"$f":"IF(LEFLevelPoA,\"Describes your level of confidence in the estimate. Modifies the resulting curve around the Most Likely.\",\"\")","v":"Describes your level of confidence in the estimate. Modifies the resulting curve around the Most Likely."}]
[null,null,null,{"$f":"(IF(COUNTIF(D9:D15,\"\")=3,1,0))","v":0}]
[null,null,"Document your rationale for the values you've chosen. (Optional)"]

{"sheet":"PL","index":11,"n_rows":83,"n_cols":15,"rows_kept":83,"cut":false,"hidden":true,"merged":[[70,2,81,12],[58,2,58,12],[60,5,60,12],[62,5,62,12],[64,5,64,12],[66,5,66,12],[1,4,1,15],[4,2,4,12],[4,14,81,14],[6,2,6,12],[8,2,8,12],[10,5,10,12],[12,5,12,12],[14,5,14,12],[16,5,16,12],[20,2,31,12],[33,2,33,12],[35,5,35,12],[37,5,37,12],[39,5,39,12],[41,5,41,12],[45,2,56,12]]}
[]
[null,null,null,null,"PRIMARY LOSS INPUT\n© FAIR Institute. All rights reserved. "]
[]
[]
[null,null,"Primary Loss",null,null,null,null,null,null,null,null,null,null,null,"Guidance:\nTo estimate PL, identify the types of primary losses that could arise, such as reduced revenue, unproductive employee time, replacement costs, and incident response team expenditures. Each form of Primary Loss should be assessed independently, using historical data, industry reports, and expert opinions to estimate the potential financial exposure. When precise data is unavailable, you can use structured estimation techniques, such as calibrated ranges or percentile-based judgments, to approximate costs.\n\nThe six (6) forms of loss in the FAIR Model are:\n\n1. Productivity Loss – The cost of reduced efficiency or downtime due to a cyber incident.\n2. Response Cost – Expenses incurred to contain, investigate, and remediate an event.\n3. Replacement Cost – The financial impact of replacing or restoring damaged or lost assets.\n4. Competitive Advantage Loss – The cost of losing proprietary information or trade secrets to competitors.\n5. Fines and Judgments – Regulatory penalties, legal settlements, or court-ordered damages.\n6. Reputation Damage – The financial impact of lost customer trust, brand erosion, or negative media coverage.\n\nHowever, only #1, #2, and #3 appear as Primary Losses. Therefore, #4, #5, and #6 do not appear on this sheet.\n\nNote that the FAIR Cyber Risk Management Framework, which includes the standard FAIR Model, integrates the FAIR Materiality Assessment Model™ (FAIR-MAM™) to assess Primary (PL) and Secondary Loss Magnitude (SLM). FAIR-MAM provides a more granular model for assessing or estimating cyber losses, linked to the six forms of loss.\n\nOnce the potential loss categories are identified, quantify their magnitude by considering key influencing factors. These may include the severity of the primary loss event, the organization’s response effectiveness, the regulatory environment, and stakeholder sensitivity to the incident. For instance, a company operating in a highly regulated industry may face steeper fines and legal costs than one in a less regulated sector. By incorporating these insights into the FAIR analysis, you can more accurately anticipate secondary financial impacts and optimize their risk management strategies.\n\nConsider using service providers or a commercial cyber risk management system to help assess SLM. Many provide and use benchmark data to help estimate SLM for specific types of scenarios, forms of loss, and other factors. Such services and tools can help reduce the burden of SLM estimations and provide more defensible estimates."]
[]
[null,null,"How much money are we likely to lose from each loss event?\nEnter values below for each of the three (3) forms of Primary Loss defined by the FAIR Model."]
[]
[null,null,"Productivity Loss (ProdL)"]
[]
[null,null,"Minimum:",null,null,"Monetary amount. Must be greater that zero and  less than or equal to Most Likely value."]
[]
[null,null,"Most Likely:",null,null,"Monetary amount.  Must be greater than or equal to Minimum value AND less than or equal to Maximum value."]
[]
[null,null,"Maximum:",null,null,"Monetary amount. Must be greater than or equal to Most Likely value."]
[]
[null,null,"Confidence:",null,null,"Describes your level of confidence in the estimate. Modifies the resulting curve around the Most Likely."]
[null,null,null,{"$f":"(IF(COUNTIF(D11:D17,\"\")=3,1,0))","v":0}]
[null,null,"Document your rationale for the values you've chosen. (Optional)"]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[null,null,"Response Cost (RespC)"]
[]
[null,null,"Minimum:",null,null,"Monetary amount. Must be greater that zero and  less than or equal to Most Likely value."]
[]
[null,null,"Most Likely:",null,null,"Monetary amount.  Must be greater than or equal to Minimum value AND less than or equal to Maximum value."]
[]
[null,null,"Maximum:",null,null,"Monetary amount. Must be greater than or equal to Most Likely value."]
[]
[null,null,"Confidence:",null,null,"Describes your level of confidence in the estimate. Modifies the resulting curve around the Most Likely."]
[null,null,null,{"$f":"(IF(COUNTIF(D36:D42,\"\")=3,1,0))","v":0}]
[null,null,"Document your rationale for the values you've chosen. (Optional)"]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[null,null,"Replacement Cost (ReplC)"]
[]
[null,null,"Minimum:",null,null,"Monetary amount. Must be greater that zero and  less than or equal to Most Likely value."]
[]
[null,null,"Most Likely:",null,null,"Monetary amount.  Must be greater than or equal to Minimum value AND less than or equal to Maximum value."]
[]
[null,null,"Maximum:",null,null,"Monetary amount. Must be greater than or equal to Most Likely value."]
[]
[null,null,"Confidence:",null,null,"Describes your level of confidence in the estimate. Modifies the resulting curve around the Most Likely."]
[null,null,null,{"$f":"(IF(COUNTIF(D61:D67,\"\")=3,1,0))","v":0}]
[null,null,"Document your rationale for the values you've chosen. (Optional)"]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[null,null,null,{"$f":"IF(PLProdLComplete+PLReplCComplete+PLRespCComplete=3,1,0)","v":0},null,{"$f":"IF(PLComplete,IF(COUNTIF(D17:D67,D17)=3,D17,\"Various\"),\"\")","v":""}]

{"sheet":"SLEF","index":12,"n_rows":17,"n_cols":15,"rows_kept":17,"cut":false,"hidden":true,"merged":[[1,4,1,15],[4,2,4,12],[4,14,29,14],[6,2,6,12],[8,5,8,12],[10,5,10,12],[12,5,12,12],[14,5,14,12],[18,2,29,12]]}
[]
[null,null,null,null,"SECONDARY LOSS EVENT FREQUENCY INPUT\n© FAIR Institute. All rights reserved. "]
[]
[]
[null,null,"Secondary Loss Event Frequency (SLEF)",null,null,null,null,null,null,null,null,null,null,null,"Guidance:\nTo estimate SLEF, determine whether secondary losses are relevant to the scenario. This involves assessing whether a primary loss event (e.g., a data breach) is likely to trigger responses from secondary stakeholders such as lawsuits, regulatory fines, or reputational damage that lead to additional costs. If such secondary effects are possible, the next step is estimating the probability of them occurring based on historical data, industry benchmarks, or expert judgment.\n\nOnce the likelihood of secondary loss is established, you should consider the conditions that influence its frequency. Factors such as the size and sensitivity of exposed data, regulatory scrutiny in the industry, media attention, and prior incidents can significantly impact the probability of secondary loss events occurring. \n\nNote that this tool uses a single SLEF value for all six (6) forms of loss. Other tools may use unique SLEF values for each form of loss.\n\nConsider using service providers or a commercial cyber risk management system to help assess SLEF. Many provide and use benchmark data to help estimate SLEF for specific types of scenarios. Such services and tools can help reduce the burden of SLEF estimations and provide more defensible estimates."]
[]
[null,null,"What percentage of primary loss events are likely to result in losses from secondary stakeholder reactions?"]
[]
[null,null,"Minimum:",null,null,"Percentage between 0% and 100%. Must be less than or equal to Most Likely value."]
[]
[null,null,"Most Likely:",null,null,"Percentage between 0% and 100%. Must be less than or equal to the Maximum value."]
[]
[null,null,"Maximum:",null,null,"Percentage between 0% and 100%. Must be greater than or equal to Most Likely value."]
[]
[null,null,"Confidence:",null,null,"Describes your level of confidence in the estimate. Modifies the resulting curve around the Most Likely."]
[null,null,null,{"$f":"(IF(COUNTIF(D9:D15,\"\")=3,1,0))","v":0}]
[null,null,"Document your rationale for the values you've chosen. (Optional)"]

{"sheet":"SLM","index":13,"n_rows":108,"n_cols":15,"rows_kept":108,"cut":false,"hidden":true,"merged":[[70,2,81,12],[8,2,8,12],[33,2,33,12],[10,5,10,12],[12,5,12,12],[14,5,14,12],[16,5,16,12],[58,2,58,12],[60,5,60,12],[62,5,62,12],[64,5,64,12],[66,5,66,12],[4,14,106,14],[1,4,1,15],[85,5,85,12],[87,5,87,12],[89,5,89,12],[91,5,91,12],[20,2,31,12],[4,2,4,12],[6,2,6,12],[95,2,106,12],[83,2,83,12],[35,5,35,12],[37,5,37,12],[39,5,39,12],[41,5,41,12],[45,2,56,12]]}
[]
[null,null,null,null,"SECONDARY LOSS MAGNITUDE INPUT\n© FAIR Institute. All rights reserved. "]
[]
[]
[null,null,"Secondary Loss Magnitude (SLM)",null,null,null,null,null,null,null,null,null,null,null,"Guidance:\nSLM refers to costs that occur as a result of secondary stakeholders' reactions to the primary loss event. They only materialize according to the Secondary Loss Event Frequency (SLEF), which you estimate on its own sheet. In other words, SLM is only included in the total Loss Magnitude (LM) as dictated by SLEF, which is often less than 100% of the time.\n\nTo estimate SLM, identify the types of secondary losses that could arise, such as legal expenses, regulatory fines, lost revenue from customer churn, or reputational damage. Each category of secondary loss should be assessed independently, using historical data, industry reports, and expert opinions to estimate the potential financial exposure. When precise data is unavailable, you can use structured estimation techniques, such as calibrated ranges or percentile-based judgments, to approximate costs.\n\nThe six (6) forms of loss in the FAIR Model are:\n\n1. Productivity Loss – The cost of reduced efficiency or downtime due to a cyber incident.\n2. Response Cost – Expenses incurred to contain, investigate, and remediate an event.\n3. Replacement Cost – The financial impact of replacing or restoring damaged or lost assets.\n4. Competitive Advantage Loss – The cost of losing proprietary information or trade secrets to competitors.\n5. Fines and Judgments – Regulatory penalties, legal settlements, or court-ordered damages.\n6. Reputation Damage – The financial impact of lost customer trust, brand erosion, or negative media coverage.\n\nHowever, only #2, #4, #5, and #6 appear as Secondary Losses. Therefore, #1 and #3 do not appear on this sheet.\n\nNote that the FAIR Cyber Risk Management Framework, which includes the standard FAIR Model, integrates the FAIR Materiality Assessment Model™ (FAIR-MAM™) to assess Primary (PL) and Secondary Loss Magnitude (SLM). FAIR-MAM provides a more granular model for assessing or estimating cyber losses, linked to the six forms of loss.\n\nOnce the potential loss categories are identified, quantify their magnitude by considering key influencing factors. These may include the severity of the primary loss event, the organization’s response effectiveness, the regulatory environment, and stakeholder sensitivity to the incident. For instance, a company operating in a highly regulated industry may face steeper fines and legal costs than one in a less regulated sector. By incorporating these insights into the FAIR analysis, you can more accurately anticipate secondary financial impacts and optimize their risk management strategies.\n\nConsider using service providers or a commercial cyber risk management system to help assess SLM. Many provide and use benchmark data to help estimate SLM for specific types of scenarios, forms of loss, and other factors. Such services and tools can help reduce the burden of SLM estimations and provide more defensible estimates."]
[]
[null,null,"How much loss is our organization likely to experience as a result of secondary stakeholders’ reactions to the primary loss event?\nEnter values below for each of the four (4) forms of Secondary Loss defined by the FAIR Model."]
[]
[null,null,"Response Cost (RespC)"]
[]
[null,null,"Minimum:",null,null,"Monetary amount. Must be greater that zero and  less than or equal to Most Likely value."]
[]
[null,null,"Most Likely:",null,null,"Monetary amount.  Must be greater than or equal to Minimum value AND less than or equal to Maximum value."]
[]
[null,null,"Maximum:",null,null,"Monetary amount. Must be greater than or equal to Most Likely value."]
[]
[null,null,"Confidence:",null,null,"Describes your level of confidence in the estimate. Modifies the resulting curve around the Most Likely."]
[null,null,null,{"$f":"(IF(COUNTIF(D11:D17,\"\")=3,1,0))","v":0}]
[null,null,"Document your rationale for the values you've chosen. (Optional)"]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[null,null,"Competitive Advantage Loss (CAdvL)"]
[]
[null,null,"Minimum:",null,null,"Monetary amount. Must be greater that zero and  less than or equal to Most Likely value."]
[]
[null,null,"Most Likely:",null,null,"Monetary amount.  Must be greater than or equal to Minimum value AND less than or equal to Maximum value."]
[]
[null,null,"Maximum:",null,null,"Monetary amount. Must be greater than or equal to Most Likely value."]
[]
[null,null,"Confidence:",null,null,"Describes your level of confidence in the estimate. Modifies the resulting curve around the Most Likely."]
[null,null,null,{"$f":"(IF(COUNTIF(D36:D42,\"\")=3,1,0))","v":0}]
[null,null,"Document your rationale for the values you've chosen. (Optional)"]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[null,null,"Fines & Judgments (FinJu)"]
[]
[null,null,"Minimum:",null,null,"Monetary amount. Must be greater that zero and  less than or equal to Most Likely value."]
[]
[null,null,"Most Likely:",null,null,"Monetary amount.  Must be greater than or equal to Minimum value AND less than or equal to Maximum value."]
[]
[null,null,"Maximum:",null,null,"Monetary amount. Must be greater than or equal to Most Likely value."]
[]
[null,null,"Confidence:",null,null,"Describes your level of confidence in the estimate. Modifies the resulting curve around the Most Likely."]
[null,null,null,{"$f":"(IF(COUNTIF(D61:D67,\"\")=3,1,0))","v":0}]
[null,null,"Document your rationale for the values you've chosen. (Optional)"]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[null,null,"Reputation Damage (RepuD)"]
[]
[null,null,"Minimum:",null,null,"Monetary amount. Must be greater that zero and  less than or equal to Most Likely value."]
[]
[null,null,"Most Likely:",null,null,"Monetary amount.  Must be greater than or equal to Minimum value AND less than or equal to Maximum value."]
[]
[null,null,"Maximum:",null,null,"Monetary amount. Must be greater than or equal to Most Likely value."]
[]
[null,null,"Confidence:",null,null,"Describes your level of confidence in the estimate. Modifies the resulting curve around the Most Likely."]
[null,null,null,{"$f":"(IF(COUNTIF(D86:D92,\"\")=3,1,0))","v":0}]
[null,null,"Document your rationale for the values you've chosen. (Optional)"]
[]
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[]
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[]
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[]
[]
[]
[]
[null,null,null,{"$f":"IF(SLMCAdvLComplete+SLMFinJuComplete+SLMRepuDComplete+SLMRespCComplete=4,1,0)","v":0}]

{"sheet":"Risk Report","index":14,"n_rows":56,"n_cols":5,"rows_kept":56,"cut":false,"hidden":true,"merged":[[42,2,42,3],[49,2,49,3],[1,4,1,15],[36,2,36,3],[37,2,37,3],[39,2,39,3],[40,2,40,3],[52,2,52,3],[54,2,54,3],[55,2,55,3],[45,2,45,3],[43,2,43,3],[48,2,48,3],[46,2,46,3],[51,2,51,3]]}
[]
[null,null,null,null,"RISK REPORT\n© FAIR Institute. All rights reserved. "]
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[]
[]
[]
[]
[]
[null,null,"Maximum:"]
[null,null,{"$f":"'Beta PERT & Monte Carlo'!CG14","v":""}]
[]
[null,null,"90th Percentile:"]
[null,null,{"$f":"'Beta PERT & Monte Carlo'!CG17","v":""}]
[]
[null,null,"Average:"]
[null,null,{"$f":"'Beta PERT & Monte Carlo'!CF14","v":""}]
[]
[null,null,"10th Percentile:"]
[null,null,{"$f":"'Beta PERT & Monte Carlo'!CE17","v":""}]
[]
[null,null,"Minimum:"]
[null,null,{"$f":"'Beta PERT & Monte Carlo'!CE14","v":""}]
[]
[null,null,"Likelihood of Any Loss"]
[null,null,{"$f":"1-(COUNTIF('Beta PERT & Monte Carlo'!CF21:CG10020,0)/COUNT('Beta PERT & Monte Carlo'!CF21:CG10020))","v":{"$error":"#DIV/0!"}}]
[]
[null,null,"Likelihood of No Loss"]
[null,null,{"$f":"(COUNTIF('Beta PERT & Monte Carlo'!CF24:CG10023,0)/COUNT('Beta PERT & Monte Carlo'!CF24:CG10023))","v":{"$error":"#DIV/0!"}}]

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[null,null,null,null,null,null,"Contact Frequency (CF)",null,null,null,"Probability of Action (PoA)",null,null,null,"Threat Event Frequeny (TEF)",null,null,null,"Threat Capability (TCap)",null,null,null,"Resistance Strength (RS)",null,null,null,"Susceptibility (Susc)",null,null,null,"Loss Event Frequeny (LEF)",null,null,null,"Secondary Loss Event Frequency (SLEF)",null,null,null,"Secondary Loss Magnitude (SLM)",null,null,null,"Secondary Loss Magnitude (SLM)",null,null,null,"Secondary Loss Magnitude (SLM)",null,null,null,"Secondary Loss Magnitude (SLM)",null,null,null,"Secondary Loss Magnitude (SLM)",null,null,null,"Secondary Loss (SL)",null,null,null,"Primary Loss (PL)",null,null,null,"Primary Loss (PL)",null,null,null,"Primary Loss (PL)",null,null,null,"Primary Loss (PL)",null,null,null,"Loss Magnitude (LM)",null,null,null,"Risk"]
[null,null,"BETA PERT & MONTE CARLO\n© FAIR Institute. All rights reserved. ",null,null,null,{"$f":"IF(LEFLevelCF,\"Minimum, Most Likely, Maximum and Confidence Factor entered by analyst.\",HYPERLINK(\"#'FAIR Model'!F20\",CONCATENATE(\"You chose to enter LEF factors at a different level: \",RIGHT(LEFLevel,LEN(LEFLevel)-3),\".\")))","v":"Minimum, Most Likely, Maximum and Confidence Factor entered by analyst."},null,null,null,{"$f":"IF(LEFLevelPoA,\"Minimum, Most Likely, Maximum and Confidence Factor entered by analyst.\",HYPERLINK(\"#'FAIR Model'!F20\",CONCATENATE(\"You chose to enter LEF factors at a different level: \",RIGHT(LEFLevel,LEN(LEFLevel)-3),\".\")))","v":"Minimum, Most Likely, Maximum and Confidence Factor entered by analyst."},null,null,null,{"$f":"IF(LEFLevelTEF,\"Minimum, Most Likely, Maximum and Confidence Factor entered by analyst.\",HYPERLINK(\"#'FAIR Model'!F20\",CONCATENATE(\"You chose to enter LEF factors at a different level: \",RIGHT(LEFLevel,LEN(LEFLevel)-3),\".\")))","v":"You chose to enter LEF factors at a different level: CF & PoA + TCap & RS (Lowest Level)."},null,null,null,{"$f":"IF(LEFLevelTCap,\"Minimum, Most Likely, Maximum and Confidence Factor entered by analyst.\",HYPERLINK(\"#'FAIR Model'!F20\",CONCATENATE(\"You chose to enter LEF factors at a different level: \",RIGHT(LEFLevel,LEN(LEFLevel)-3),\".\")))","v":"Minimum, Most Likely, Maximum and Confidence Factor entered by analyst."},null,null,null,{"$f":"IF(LEFLevelRS,\"Minimum, Most Likely, Maximum and Confidence Factor entered by analyst.\",HYPERLINK(\"#'FAIR Model'!F20\",CONCATENATE(\"You chose to enter LEF factors at a different level: \",RIGHT(LEFLevel,LEN(LEFLevel)-3),\".\")))","v":"Minimum, Most Likely, Maximum and Confidence Factor entered by analyst."},null,null,null,{"$f":"IF(LEFLevelSusc,\"Minimum, Most Likely, Maximum and Confidence Factor entered by analyst.\",HYPERLINK(\"#'FAIR Model'!F20\",CONCATENATE(\"You chose to enter LEF factors at a different level: \",RIGHT(LEFLevel,LEN(LEFLevel)-3),\".\")))","v":"You chose to enter LEF factors at a different level: CF & PoA + TCap & RS (Lowest Level)."},null,null,null,{"$f":"IF(LEFLevelLEF,\"Minimum, Most Likely, Maximum and Confidence Factor entered by analyst.\",HYPERLINK(\"#'FAIR Model'!F20\",CONCATENATE(\"You chose to enter LEF factors at a different level: \",RIGHT(LEFLevel,LEN(LEFLevel)-3),\".\")))","v":"You chose to enter LEF factors at a different level: CF & PoA + TCap & RS (Lowest Level)."},null,null,null,null,null,null,null,"Total of 4 Forms of Loss >>",null,null,null,{"$f":"SLM!$C9","v":"Response Cost (RespC)"},null,null,null,{"$f":"SLM!$C34","v":"Competitive Advantage Loss (CAdvL)"},null,null,null,{"$f":"SLM!$C59","v":"Fines & Judgments (FinJu)"},null,null,null,{"$f":"SLM!$C84","v":"Reputation Damage (RepuD)"},null,null,null,null,null,null,null,"Total of 3 Forms of Loss >>",null,null,null,{"$f":"PL!$C9","v":"Productivity Loss (ProdL)"},null,null,null,{"$f":"PL!$C34","v":"Response Cost (RespC)"},null,null,null,{"$f":"PL!$C59","v":"Replacement Cost (ReplC)"}]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Minimum, Most Likely, Maximum and Confidence Factor entered by analyst.",null,null,null,"Based on summation of Monte Carlo simulated values from each form of loss.",null,null,null,"Minimum, Most Likely, Maximum and Confidence Factor entered by analyst.",null,null,null,"Minimum, Most Likely, Maximum and Confidence Factor entered by analyst.",null,null,null,"Minimum, Most Likely, Maximum and Confidence Factor entered by analyst.",null,null,null,"Minimum, Most Likely, Maximum and Confidence Factor entered by analyst.",null,null,null,"Based on Monte Carlo simulations of\nSLM and SLEF.",null,null,null,"Based on summation of Monte Carlo simulated values from each form of loss.",null,null,null,"Minimum, Most Likely, Maximum and Confidence Factor entered by analyst.",null,null,null,"Minimum, Most Likely, Maximum and Confidence Factor entered by analyst.",null,null,null,"Minimum, Most Likely, Maximum and Confidence Factor entered by analyst.",null,null,null,"Minimum, Average, and Maximum values derived from PL and SL simulations.",null,null,null,"Minimum, Average, and Maximum values derived from LEF and LM simulations."]
[]
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[null,"This sheet uses Monte Carlo simulations to model uncertainty in FAIR factors like Loss Magnitude (LM) and Threat Event Frequency (TEF). You'll find each factor in the columns to the right. This sheet uses Beta PERT distributions to calculate likely (probable) values. In some cases, as with TEF, this sheet uses a Bernoulli trial to create whole integer values for the Beta PERT values.\n\nWhy Beta PERT?\nIt’s used to model expert estimates where we have:\n   - Minimum (optimistic scenario)\n   - Most Likely (expected value)\n   - Maximum (worst case)\n\nThe formula for the Beta PERT Mean is:\n   Mean = (Minimum + (4 x MostLikely) + Maximum) / 6\nThis smooths out extreme values while keeping the estimate realistic.\n\nWhy Use a Bernoulli Trial?\nA Bernoulli trial is used when analyzing binary (yes/no, success/failure) outcomes in probability and statistics. It helps model random events that have only two possible outcomes and is commonly applied in FAIR risk assessments for determining event probabilities.\n\nA Bernoulli test is useful when:\n1. You need to model an event that either happens or doesn’t (e.g., a cyberattack succeeds or fails).\n2. You need to estimate event frequencies over time (e.g., how often a specific threat occurs in a given period).\n3. You want to run Monte Carlo simulations with probabilistic inputs instead of assuming fixed numbers.\n\nMonte Carlo Simulations\nThe spreadsheet randomly samples values from a Beta PERT distribution to generate 10,000 possible risk outcomes. The final risk value is determined by summarizing these simulations (e.g., taking the median or most likely value).",null,null,"Quick Links to Factor Calculations",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF(SLEFComplete,SLEF!$D9,\"\")","v":""},{"$f":"IF(SLEFComplete,SLEF!$D11,\"\")","v":""},{"$f":"IF(SLEFComplete,IF(SLEF!$D13=0,1E-22,SLEF!$D13),\"\")","v":""},null,{"$f":"IF(SLMComplete,AQ7+AU7+AY7+BC7,\"\")","v":""},{"$f":"IF(SLMComplete,AR7+AV7+AZ7+BD7,\"\")","v":""},{"$f":"IF(SLMComplete,AS7+AW7+BA7+BE7,\"\")","v":""},null,{"$f":"IF(SLMRespCComplete,SLM!$D11,\"\")","v":""},{"$f":"IF(SLMRespCComplete,SLM!$D13,\"\")","v":""},{"$f":"IF(SLMRespCComplete,IF(SLM!$D15=0,0.01,SLM!$D15),\"\")","v":""},null,{"$f":"IF(SLMCAdvLComplete,SLM!$D36,\"\")","v":""},{"$f":"IF(SLMCAdvLComplete,SLM!$D38,\"\")","v":""},{"$f":"IF(SLMCAdvLComplete,IF(SLM!$D40=0,0.01,SLM!$D40),\"\")","v":""},null,{"$f":"IF(SLMFinJuComplete,SLM!$D61,\"\")","v":""},{"$f":"IF(SLMFinJuComplete,SLM!$D63,\"\")","v":""},{"$f":"IF(SLMFinJuComplete,IF(SLM!$D65=0,0.01,SLM!$D65),\"\")","v":""},null,{"$f":"IF(SLMRepuDComplete,SLM!$D86,\"\")","v":""},{"$f":"IF(SLMRepuDComplete,SLM!$D88,\"\")","v":""},{"$f":"IF(SLMRepuDComplete,IF(SLM!$D90=0,0.01,SLM!$D90),\"\")","v":""},null,"N/A","N/A","N/A",null,{"$f":"IF(PLComplete,BO7+BS7+BW7,\"\")","v":""},{"$f":"IF(PLComplete,BP7+BT7+BX7,\"\")","v":""},{"$f":"IF(PLComplete,BQ7+BU7+BY7,\"\")","v":""},null,{"$f":"IF(PLProdLComplete,PL!$D11,\"\")","v":""},{"$f":"IF(PLProdLComplete,PL!$D13,\"\")","v":""},{"$f":"IF(PLProdLComplete,IF(PL!$D15=0,0.01,PL!$D15),\"\")","v":""},null,{"$f":"IF(PLRespCComplete,PL!$D36,\"\")","v":""},{"$f":"IF(PLRespCComplete,PL!$D38,\"\")","v":""},{"$f":"IF(PLRespCComplete,IF(PL!$D40=0,0.01,PL!$D40),\"\")","v":""},null,{"$f":"IF(PLReplCComplete,PL!$D61,\"\")","v":""},{"$f":"IF(PLReplCComplete,PL!$D63,\"\")","v":""},{"$f":"IF(PLReplCComplete,IF(PL!$D65=0,0.01,PL!$D65),\"\")","v":""},null,"N/A","N/A","N/A",null,"N/A","N/A","N/A"]
[null,null,null,null,null,null,"Conf Factor","Alpha","Beta",null,"Conf Factor","Alpha","Beta",null,"Conf Factor","Alpha","Beta",null,"Conf Factor","Alpha","Beta",null,"Conf Factor","Alpha","Beta",null,"Conf Factor","Alpha","Beta",null,"Conf Factor","Alpha","Beta"]
[null,null,null,null,{"$f":"G1","v":"Contact Frequency (CF)"},null,{"$f":"IF(CFComplete,IF(LEFLevelCF,IF(CFConfidence=\"Low\",1,IF(CFConfidence=\"Medium\",4,IF(CFConfidence=\"High\",8,\"Incomplete\"))),\"N/A\"),\"\")","v":""},{"$f":"IF(CFComplete,IF(LEFLevelCF,1 + (G9 * (H6 - G6) / (I6 - G6)),\"N/A\"),\"\")","v":""},{"$f":"IF(CFComplete,IF(LEFLevelCF,1 + (G9 * (I6 - H6) / (I6 - G6)),\"N/A\"),\"\")","v":""},null,{"$f":"IF(PoAComplete,IF(LEFLevelPoA,IF(PoAConfidence=\"Low\",1,IF(PoAConfidence=\"Medium\",4,IF(PoAConfidence=\"High\",8,\"Incomplete\"))),\"N/A\"),\"\")","v":""},{"$f":"IF(PoAComplete,IF(LEFLevelPoA,1 + (K9 * (L6 - K6) / (M6 - K6)),\"N/A\"),\"\")","v":""},{"$f":"IF(PoAComplete,IF(LEFLevelPoA,1 + (K9 * (M6 - L6) / (M6 - K6)),\"N/A\"),\"\")","v":""},null,{"$f":"IF(LEFLevelTEF,IF(TEFConfidence=\"Low\",1,IF(TEFConfidence=\"Medium\",4,IF(TEFConfidence=\"High\",8,\"Incomplete\"))),\"N/A\")","v":"N/A"},{"$f":"IF(LEFLevelTEF,1 + (O9 * (P6 - O6) / (Q6 - O6)),\"N/A\")","v":"N/A"},{"$f":"IF(LEFLevelTEF,1 + (O9 * (Q6 - P6) / (Q6 - O6)),\"N/A\")","v":"N/A"},null,{"$f":"IF(TCapComplete,IF(LEFLevelTCap,IF(TCapConfidence=\"Low\",1,IF(TCapConfidence=\"Medium\",4,IF(TCapConfidence=\"High\",8,\"Incomplete\"))),\"N/A\"),\"\")","v":""},{"$f":"IF(TCapComplete,IF(LEFLevelTCap,1 + (S9 * (T6 - S6) / (U6 - S6)),\"N/A\"),\"\")","v":""},{"$f":"IF(TCapComplete,IF(LEFLevelTCap,1 + (S9 * (U6 - T6) / (U6 - S6)),\"N/A\"),\"\")","v":""},null,{"$f":"IF(RSComplete,IF(LEFLevelRS,IF(RSConfidence=\"Low\",1,IF(RSConfidence=\"Medium\",4,IF(RSConfidence=\"High\",8,\"Incomplete\"))),\"N/A\"),\"\")","v":""},{"$f":"IF(RSComplete,IF(LEFLevelRS,1 + (W9 * (X6 - W6) / (Y6 - W6)),\"N/A\"),\"\")","v":""},{"$f":"IF(RSComplete,IF(LEFLevelRS,1 + (W9 * (Y6 - X6) / (Y6 - W6)),\"N/A\"),\"\")","v":""},null,{"$f":"IF(LEFLevelSusc,IF(SuscConfidence=\"Low\",1,IF(SuscConfidence=\"Medium\",4,IF(SuscConfidence=\"High\",8,\"Incomplete\"))),\"N/A\")","v":"N/A"},{"$f":"IF(LEFLevelSusc,1 + (AA9 * (AB6 - AA6) / (AC6 - AA6)),\"N/A\")","v":"N/A"},{"$f":"IF(LEFLevelSusc,1 + (AA9 * (AC6 - AB6) / (AC6 - AA6)),\"N/A\")","v":"N/A"},null,{"$f":"IF(LEFLevelLEF,IF(LEFConfidence=\"Low\",1,IF(LEFConfidence=\"Medium\",4,IF(LEFConfidence=\"High\",8,\"Incomplete\"))),\"N/A\")","v":"N/A"},{"$f":"IF(LEFLevelLEF,1 + (AE9 * (AF6 - AE6) / (AG6 - AE6)),\"N/A\")","v":"N/A"},{"$f":"IF(LEFLevelLEF,1 + (AE9 * (AG6 - AF6) / (AG6 - AE6)),\"N/A\")","v":"N/A"},null,"Conf Factor","Alpha","Beta",null,"Conf Factor","Alpha","Beta",null,"Conf Factor","Alpha","Beta",null,"Conf Factor","Alpha","Beta",null,"Conf Factor","Alpha","Beta",null,"Conf Factor","Alpha","Beta",null,"Conf Factor","Alpha","Beta",null,"Conf Factor","Alpha","Beta",null,"Conf Factor","Alpha","Beta",null,"Conf Factor","Alpha","Beta",null,"Conf Factor","Alpha","Beta",null,"Conf Factor","Alpha","Beta",null,"Conf Factor","Alpha","Beta"]
[null,null,null,null,{"$f":"K1","v":"Probability of Action (PoA)"},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF(SLEFComplete,IF(SLEFConfidence=\"Low\",1,IF(SLEFConfidence=\"Medium\",4,IF(SLEFConfidence=\"High\",8,\"Incomplete\"))),\"\")","v":""},{"$f":"IF(SLEFComplete,1 + (AI10 * (AJ7 - AI7) / (AK7 - AI7)),\"\")","v":""},{"$f":"IF(SLEFComplete,1 + (AI10 * (AK7 - AJ7) / (AK7 - AI7)),\"\")","v":""},null,"N/A","N/A","N/A",null,{"$f":"IF(SLMRespCComplete,IF(SLMRespCConfidence=\"Low\",1,IF(SLMRespCConfidence=\"Medium\",4,IF(SLMRespCConfidence=\"High\",8,\"Incomplete\"))),\"\")","v":""},{"$f":"IF(SLMRespCComplete,IF(SUM(AQ7:AS7)>0,1 + (AQ10 * (AR7 - AQ7) / (AS7 - AQ7)),\"N/A\"),\"\")","v":""},{"$f":"IF(SLMRespCComplete,IF(SUM(AQ7:AS7)>0,1 + (AQ10 * (AS7 - AR7) / (AS7 - AQ7)),\"N/A\"),\"\")","v":""},null,{"$f":"IF(SLMCAdvLComplete,IF(SLMCAdvLConfidence=\"Low\",1,IF(SLMCAdvLConfidence=\"Medium\",4,IF(SLMCAdvLConfidence=\"High\",8,\"Incomplete\"))),\"\")","v":""},{"$f":"IF(SLMCAdvLComplete,IF(SUM(AU7:AW7)>0,1 + (AU10 * (AV7 - AU7) / (AW7 - AU7)),\"N/A\"),\"\")","v":""},{"$f":"IF(SLMCAdvLComplete,IF(SUM(AU7:AW7)>0,1 + (AU10 * (AW7 - AV7) / (AW7 - AU7)),\"N/A\"),\"\")","v":""},null,{"$f":"IF(SLMFinJuComplete,IF(SLMFinJuConfidence=\"Low\",1,IF(SLMFinJuConfidence=\"Medium\",4,IF(SLMFinJuConfidence=\"High\",8,\"Incomplete\"))),\"\")","v":""},{"$f":"IF(SLMFinJuComplete,IF(SUM(AY7:BA7)>0,1 + (AY10 * (AZ7 - AY7) / (BA7 - AY7)),\"N/A\"),\"\")","v":""},{"$f":"IF(SLMFinJuComplete,IF(SUM(AY7:BA7)>0,1 + (AY10 * (BA7 - AZ7) / (BA7 - AY7)),\"N/A\"),\"\")","v":""},null,{"$f":"IF(SLMRepuDComplete,IF(SLMRepuDConfidence=\"Low\",1,IF(SLMRepuDConfidence=\"Medium\",4,IF(SLMRepuDConfidence=\"High\",8,\"Incomplete\"))),\"\")","v":""},{"$f":"IF(SLMRepuDComplete,IF(SUM(BC7:BE7)>0,1 + (BC10 * (BD7 - BC7) / (BE7 - BC7)),\"N/A\"),\"\")","v":""},{"$f":"IF(SLMRepuDComplete,IF(SUM(BC7:BE7)>0,1 + (BC10 * (BE7 - BD7) / (BE7 - BC7)),\"N/A\"),\"\")","v":""},null,"N/A","N/A","N/A",null,"N/A","N/A","N/A",null,{"$f":"IF(PLProdLComplete,IF(PLProdLConfidence=\"Low\",1,IF(PLProdLConfidence=\"Medium\",4,IF(PLProdLConfidence=\"High\",8,\"Incomplete\"))),\"\")","v":""},{"$f":"IF(PLProdLComplete,IF(SUM(BO7:BQ7)>0,1 + (BO10 * (BP7 - BO7) / (BQ7 - BO7)),\"N/A\"),\"\")","v":""},{"$f":"IF(PLProdLComplete,IF(SUM(BO7:BQ7)>0,1 + (BO10 * (BQ7 - BP7) / (BQ7 - BO7)),\"N/A\"),\"\")","v":""},null,{"$f":"IF(PLRespCComplete,IF(PLRespCConfidence=\"Low\",1,IF(PLRespCConfidence=\"Medium\",4,IF(PLRespCConfidence=\"High\",8,\"Incomplete\"))),\"\")","v":""},{"$f":"IF(PLRespCComplete,IF(SUM(BS7:BU7)>0,1 + (BS10 * (BT7 - BS7) / (BU7 - BS7)),\"N/A\"),\"\")","v":""},{"$f":"IF(PLRespCComplete,IF(SUM(BS7:BU7)>0,1 + (BS10 * (BU7 - BT7) / (BU7 - BS7)),\"N/A\"),\"\")","v":""},null,{"$f":"IF(PLReplCComplete,IF(PLReplCConfidence=\"Low\",1,IF(PLReplCConfidence=\"Medium\",4,IF(PLReplCConfidence=\"High\",8,\"Incomplete\"))),\"\")","v":""},{"$f":"IF(PLReplCComplete,IF(SUM(BW7:BY7)>0,1 + (BW10 * (BX7 - BW7) / (BY7 - BW7)),\"N/A\"),\"\")","v":""},{"$f":"IF(PLReplCComplete,IF(SUM(BW7:BY7)>0,1 + (BW10 * (BY7 - BX7) / (BY7 - BW7)),\"N/A\"),\"\")","v":""},null,"N/A","N/A","N/A",null,"N/A","N/A","N/A"]
[null,null,null,null,{"$f":"O1","v":"Threat Event Frequeny (TEF)"},null,"Monte Carlo Sim Stats",null,null,null,"Monte Carlo Sim Stats",null,null,null,"Monte Carlo Sim Stats",null,null,null,"NOTE: Simuluated values below are only used for generating the historgram chart for TCap. The TCap and RS Beta PERTs that determine the Susc values are combined in a single cell (calculation) and thus cannot be charted separately. The values below are therefore for illustration purposes only.",null,null,null,"NOTE: Simuluated values below are only used for generating the historgram chart for RS. The TCap and RS Beta PERTs that determine the Susc values are combined in a single cell (calculation) and thus cannot be charted separately. The values below are therefore for illustration purposes only.",null,null,null,"Monte Carlo Sim Stats",null,null,null,"Monte Carlo Sim Stats"]
[null,null,null,null,{"$f":"S1","v":"Threat Capability (TCap)"},null,"Minimum","Average","Maximum",null,"Minimum","Average","Maximum",null,"Minimum","Average","Maximum",null,null,null,null,null,null,null,null,null,"Minimum","Average","Maximum",null,"Minimum","Average","Maximum",null,"Monte Carlo Sim Stats",null,null,null,"Monte Carlo Sim Stats",null,null,null,"Monte Carlo Sim Stats",null,null,null,"Monte Carlo Sim Stats",null,null,null,"Monte Carlo Sim Stats",null,null,null,"Monte Carlo Sim Stats",null,null,null,"Monte Carlo Sim Stats",null,null,null,"Monte Carlo Sim Stats",null,null,null,"Monte Carlo Sim Stats",null,null,null,"Monte Carlo Sim Stats",null,null,null,"Monte Carlo Sim Stats",null,null,null,"Monte Carlo Sim Stats",null,null,null,"Monte Carlo Sim Stats"]
[null,null,null,null,{"$f":"W1","v":"Resistance Strength (RS)"},null,{"$f":"IF(CFComplete,IF(LEFLevelCF,MIN(H20:H10019),\"N/A\"),\"\")","v":""},{"$f":"IF(CFComplete,IF(LEFLevelCF,AVERAGE(H20:H10019),\"N/A\"),\"\")","v":""},{"$f":"IF(CFComplete,IF(LEFLevelCF,MAX(H20:H10019),\"N/A\"),\"\")","v":""},null,{"$f":"IF(PoAComplete,IF(LEFLevelPoA,MIN(L20:L10019),\"N/A\"),\"\")","v":""},{"$f":"IF(PoAComplete,IF(LEFLevelPoA,AVERAGE(L20:L10019),\"N/A\"),\"\")","v":""},{"$f":"IF(PoAComplete,IF(LEFLevelPoA,MAX(L20:L10019),\"N/A\"),\"\")","v":""},null,{"$f":"IF(LEFLevelLEF,\"N/A\",MIN(P20:P10019))","v":{"$error":"#VALUE!"}},{"$f":"IF(LEFLevelLEF,\"N/A\",AVERAGE(P20:P10019))","v":{"$error":"#VALUE!"}},{"$f":"IF(LEFLevelLEF,\"N/A\",MAX(P20:P10019))","v":{"$error":"#VALUE!"}},null,null,null,null,null,null,null,null,null,{"$f":"IF(LEFLevelLEF,\"N/A\",MIN(AB20:AB10019))","v":{"$error":"#VALUE!"}},{"$f":"IF(LEFLevelLEF,\"N/A\",AVERAGE(AB20:AB10019))","v":{"$error":"#VALUE!"}},{"$f":"IF(LEFLevelLEF,\"N/A\",MAX(AB20:AB10019))","v":{"$error":"#VALUE!"}},null,{"$f":"MIN(AG20:AG10019)","v":{"$error":"#VALUE!"}},{"$f":"AVERAGE(AG20:AG10019)","v":{"$error":"#VALUE!"}},{"$f":"MAX(AG20:AG10019)","v":{"$error":"#VALUE!"}},null,"Minimum","Average","Maximum",null,"Minimum","Average","Maximum",null,"Minimum","Average","Maximum",null,"Minimum","Average","Maximum",null,"Minimum","Average","Maximum",null,"Minimum","Average","Maximum",null,"Minimum","Average","Maximum",null,"Minimum","Average","Maximum",null,"Minimum","Average","Maximum",null,"Minimum","Average","Maximum",null,"Minimum","Average","Maximum",null,"Minimum","Average","Maximum",null,"Minimum","Average","Maximum"]
[null,null,null,null,{"$f":"AA1","v":"Susceptibility (Susc)"},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF(SLEFComplete,MIN(AJ21:AJ10020),\"\")","v":""},{"$f":"IF(SLEFComplete,AVERAGE(AJ21:AJ10020),\"\")","v":""},{"$f":"IF(SLEFComplete,MAX(AJ21:AJ10020),\"\")","v":""},null,{"$f":"IF(SLMComplete,IF(ISNUMBER(SEARCH(\"SLM\",LEFLevel,1)),MIN(AN21:AN10020),\"N/A\"),\"\")","v":""},{"$f":"IF(SLMComplete,IF(ISNUMBER(SEARCH(\"SLM\",LEFLevel,1)),AVERAGE(AN21:AN10020),\"N/A\"),\"\")","v":""},{"$f":"IF(SLMComplete,IF(ISNUMBER(SEARCH(\"SLM\",LEFLevel,1)),MAX(AN21:AN10020),\"N/A\"),\"\")","v":""},null,{"$f":"IF(SLMRespCComplete,MIN(AR21:AR10020),\"\")","v":""},{"$f":"IF(SLMRespCComplete,AVERAGE(AR21:AR10020),\"\")","v":""},{"$f":"IF(SLMRespCComplete,MAX(AR21:AR10020),\"\")","v":""},null,{"$f":"IF(SLMCAdvLComplete,MIN(AV21:AV10020),\"\")","v":""},{"$f":"IF(SLMCAdvLComplete,AVERAGE(AV21:AV10020),\"\")","v":""},{"$f":"IF(SLMCAdvLComplete,MAX(AV21:AV10020),\"\")","v":""},null,{"$f":"IF(SLMFinJuComplete,MIN(AZ21:AZ10020),\"\")","v":""},{"$f":"IF(SLMFinJuComplete,AVERAGE(AZ21:AZ10020),\"\")","v":""},{"$f":"IF(SLMFinJuComplete,MAX(AZ21:AZ10020),\"\")","v":""},null,{"$f":"IF(SLMRepuDComplete,MIN(BD21:BD10020),\"\")","v":""},{"$f":"IF(SLMRepuDComplete,AVERAGE(BD21:BD10020),\"\")","v":""},{"$f":"IF(SLMRepuDComplete,MAX(BD21:BD10020),\"\")","v":""},null,{"$f":"IF(SLComplete,MIN(BH21:BH10020),\"\")","v":""},{"$f":"IF(SLComplete,AVERAGE(BH146:BH10020),\"\")","v":""},{"$f":"IF(SLComplete,MAX(BH146:BH10020),\"\")","v":""},null,{"$f":"IF(PLComplete,MIN(BL21:BL10020),\"\")","v":""},{"$f":"IF(PLComplete,AVERAGE(BL21:BL10020),\"\")","v":""},{"$f":"IF(PLComplete,MAX(BL21:BL10020),\"\")","v":""},null,{"$f":"IF(PLProdLComplete,MIN(BP21:BP10020),\"\")","v":""},{"$f":"IF(PLProdLComplete,AVERAGE(BP21:BP10020),\"\")","v":""},{"$f":"IF(PLProdLComplete,MAX(BP21:BP10020),\"\")","v":""},null,{"$f":"IF(PLRespCComplete,MIN(BT46:BT10020),\"\")","v":""},{"$f":"IF(PLRespCComplete,AVERAGE(BT46:BT10020),\"\")","v":""},{"$f":"IF(PLRespCComplete,MAX(BT46:BT10020),\"\")","v":""},null,{"$f":"IF(PLReplCComplete,MIN(BX71:BX10020),\"\")","v":""},{"$f":"IF(PLReplCComplete,AVERAGE(BX71:BX10020),\"\")","v":""},{"$f":"IF(PLReplCComplete,MAX(BX71:BX10020),\"\")","v":""},null,{"$f":"IF(LMComplete,MIN(CB146:CB10020),\"\")","v":""},{"$f":"IF(LMComplete,AVERAGE(CB146:CB10020),\"\")","v":""},{"$f":"IF(LMComplete,MAX(CB146:CB10020),\"\")","v":""},null,{"$f":"IF(RiskComplete,MIN(CF146:CF10020),\"\")","v":""},{"$f":"IF(RiskComplete,AVERAGE(CF146:CF10020),\"\")","v":""},{"$f":"IF(RiskComplete,MAX(CF146:CF10020),\"\")","v":""}]
[null,null,null,null,{"$f":"AE1","v":"Loss Event Frequeny (LEF)"},null,"P10","Std Dev","P90",null,"P10","Std Dev","P90",null,"P10","Std Dev","P90",null,null,null,null,null,null,null,null,null,"P10","Std Dev","P90",null,"P10","Std Dev","P90",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF(SLMComplete+SLEFComplete=2,1,0)","v":0},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF(SLEFComplete+SLMComplete+PLComplete=3,1,0)","v":0}]
[null,null,null,null,{"$f":"AI1","v":"Secondary Loss Event Frequency (SLEF)"},null,{"$f":"IF(CFComplete,IF(LEFLevelCF,_xlfn.PERCENTILE.INC(H20:H10019, 0.1),\"N/A\"),\"\")","v":""},{"$f":"IF(CFComplete,IF(LEFLevelCF,STDEV(H20:H10019),\"N/A\"),\"\")","v":""},{"$f":"IF(CFComplete,IF(LEFLevelCF,_xlfn.PERCENTILE.INC(H20:H10019, 0.9),\"N/A\"),\"\")","v":""},null,{"$f":"IF(PoAComplete,IF(LEFLevelPoA,_xlfn.PERCENTILE.INC(L20:L10019, 0.1),\"N/A\"),\"\")","v":""},{"$f":"IF(PoAComplete,IF(LEFLevelPoA,STDEV(L20:M10019),\"N/A\"),\"\")","v":""},{"$f":"IF(PoAComplete,IF(LEFLevelPoA,_xlfn.PERCENTILE.INC(L20:L10019, 0.9),\"N/A\"),\"\")","v":""},null,{"$f":"IF(LEFLevelLEF,\"N/A\",_xlfn.PERCENTILE.INC(Q20:Q10019, 0.1))","v":{"$error":"#VALUE!"}},{"$f":"IF(LEFLevelLEF,\"N/A\",STDEV(Q20:Q10019))","v":{"$error":"#VALUE!"}},{"$f":"IF(LEFLevelLEF,\"N/A\",_xlfn.PERCENTILE.INC(Q20:Q10019, 0.9))","v":{"$error":"#VALUE!"}},null,null,null,null,null,null,null,null,null,{"$f":"IF(LEFLevelLEF,\"N/A\",_xlfn.PERCENTILE.INC(AB20:AB10019, 0.1))","v":{"$error":"#VALUE!"}},{"$f":"IF(LEFLevelLEF,\"N/A\",STDEV(AB20:AB10019))","v":{"$error":"#VALUE!"}},{"$f":"IF(LEFLevelLEF,\"N/A\",_xlfn.PERCENTILE.INC(AB20:AB10019, 0.9))","v":{"$error":"#VALUE!"}},null,{"$f":"IF(LEFLevelLEF,\"N/A\",_xlfn.PERCENTILE.INC(AG20:AG10019, 0.1))","v":{"$error":"#VALUE!"}},{"$f":"IF(LEFLevelLEF,\"N/A\",STDEV(AF20:AF10019))","v":{"$error":"#VALUE!"}},{"$f":"IF(LEFLevelLEF,\"N/A\",_xlfn.PERCENTILE.INC(AF20:AF10019, 0.9))","v":{"$error":"#VALUE!"}},null,"P10","Std Dev","P90",null,"P10","Std Dev","P90",null,"P10","Std Dev","P90",null,"P10","Std Dev","P90",null,"P10","Std Dev","P90",null,"P10","Std Dev","P90",null,"P10","Std Dev","P90",null,"P10","Std Dev","P90",null,"P10","Std Dev","P90",null,"P10","Std Dev","P90",null,"P10","Std Dev","P90",null,"P10","Std Dev","P90",null,"P10","Std Dev","P90"]
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{"sheet":"Test Data Generator","index":16,"n_rows":99,"n_cols":4,"rows_kept":99,"cut":false,"hidden":true,"merged":[]}
[]
[null,"FAIR-U Workbook for Learners\n© FAIR Institute. All rights reserved. "]
[]
["Named Range","Test Scenario 1","Test Scenario 2","Test Scenario 3"]
["AnalysisName","Payment System Data Breach Risk Assessment","Intellectual Property Theft Risk Assessment","Cloud Data Exposure Risk Assessment"]
["AnalysisAsset","Digital Payment Infrastructure & Customer Financial Data: This asset represents the organization’s payment processing infrastructure, including online transaction systems, merchant payment gateways, and cardholder data storage. It is mission-critical for revenue generation, as it enables secure transactions between customers and the business. A compromise could lead to direct financial losses, fraudulent transactions, regulatory fines, and long-term reputational damage due to loss of customer trust.","Proprietary Product Designs & R&D Competitive Intelligence: This asset consists of high-value intellectual property, including proprietary engineering blueprints, product development roadmaps, and strategic research data. It directly contributes to the company’s competitive advantage and future revenue streams. The unauthorized disclosure of this asset to competitors or adversarial nations could result in market share erosion, financial losses from counterfeit products, and potential legal liabilities related to intellectual property protection.","Cloud Storage System & Confidential Customer Records: This asset includes cloud-based storage environments where sensitive customer data, including personally identifiable information (PII) and financial records, is stored. It is integral to operations, compliance, and customer trust. Exposure could lead to regulatory fines, legal action, and reputational damage."]
["AnalysisCreateDate","2025-03-16","2025-03-16","2025-03-16"]
["AnalysisCurrency","Dollars (All Countries)","British Sterling Pounds","Euros"]
["AnalysisEffect","Potential financial loss due to fraudulent transactions and reputational damage.","Potential loss of competitive advantage, regulatory scrutiny, and revenue impact due to leaked product designs.","Unauthorized public access to sensitive customer data, leading to financial, legal, and reputational consequences."]
["AnalysisMethod","Credential Stuffing & Web Exploitation Leading to Data Exfiltration: Attackers leverage credential stuffing and web application vulnerabilities to gain unauthorized access to the payment processing system. Automated bots test stolen credentials against login portals, exploiting weak authentication mechanisms. Additionally, SQL injection and API abuse enable attackers to bypass security controls and exfiltrate sensitive customer financial data. The breach results in fraudulent transactions, regulatory penalties, and reputational harm.","Spear Phishing & Privileged Account Takeover Leading to Data Exfiltration: A nation-state threat actor conducts targeted spear phishing campaigns against R&D personnel, tricking employees into disclosing credentials or executing malware. Once inside the network, attackers escalate privileges through credential harvesting and exploit misconfigured access controls to infiltrate R&D systems. Using encrypted data transfer techniques, they exfiltrate proprietary engineering blueprints and research data, leading to loss of competitive advantage and regulatory scrutiny.","Cloud Misconfiguration & Unauthorized Access Leading to Data Leakage – Misconfigured cloud storage settings result in unrestricted public access, exposing confidential records to unauthorized parties. Threat actors leverage automated scanning tools to identify exposed buckets, exfiltrating data for financial fraud, identity theft, or resale on dark web marketplaces."]
["AnalysisMotiveIntent","Financial gain by cybercriminals attempting to exploit payment processing vulnerabilities.","Espionage-driven threat actor aiming to exfiltrate and sell proprietary information.","Financial gain through data resale, identity theft, or extortion by ransomware groups."]
["AnalysisPeriodEnd","2025-12-31","2025-12-31","2025-12-31"]
["AnalysisPeriodStart","2025-01-01","2025-01-01","2025-01-01"]
["AnalysisPurpose","Evaluate cyber risk exposure from unauthorized access to payment data and inform mitigation strategies.","Evaluate the risk exposure of R&D data theft and guide investments in data loss prevention controls.","Assess risk exposure from cloud misconfigurations and develop strategies to enhance data security and access controls."]
["AnalysisThreatCapability","Highly skilled cybercriminal group utilizing advanced exploit kits.","Highly skilled state-sponsored hacking group with access to sophisticated attack tools.","Moderate to high – Threat actors leverage open-source tools to scan for misconfigured cloud storage, requiring minimal expertise but yielding high impact."]
["AnalysisThreatContact","Regular","Regular","Regular"]
["AnalysisThreatDescription","Cybercriminals leveraging phishing and credential stuffing attacks to compromise payment systems.","Advanced persistent threat (APT) actors conducting targeted attacks using phishing, credential theft, and zero-day exploits.","Cybercriminals or rogue insiders exploiting misconfigured access controls to exfiltrate sensitive data for financial gain."]
["AnalysisThreatName","Organized Cybercrime Group (e.g., FIN7, Magecart).","APT-XYZ (Suspected Nation-State Group)","Opportunistic Data Harvesters & Cybercrime Syndicates"]
["AnalysisThreatRiskTolerance","High - Threat actor is operating outside the borders of Western countries and has little to lose.","High – Threat actors operate under state protection, minimizing legal and operational risks.","High – Threat actors face minimal consequences due to the anonymous nature of data exfiltration."]
["AnalysisThreatSponsorship","Self-funded criminal operation seeking financial gain.","Government-backed cyber espionage group seeking economic and strategic intelligence.","Independent cybercriminals or loosely affiliated groups specializing in data breaches and fraud."]
["AnalysisThreatTargets","Payment processing infrastructure, customer financial data, and administrator credentials.","Proprietary engineering blueprints, product development roadmaps, and research data.","Cloud storage environments containing customer records, internal communications, and corporate intellectual property."]
["AnalysisThreatType","Malicious","Malicious","Malicious"]
["AnalysisUpdateDate","2025-03-16","2025-03-16","2025-03-16"]
["CFConfidence","Medium","High","Medium"]
["CFMaximum",50,50,60]
["CFMinimum",5,10,5]
["CFMostLikely",20,30,25]
["CFRationale","Past attack trends and industry threat intelligence indicate frequent attempts.","Regular reconnaissance and previous intrusion attempts indicate a high level of attacker interest.","Cloud security misconfigurations are common, and attackers regularly scan for vulnerabilities."]
["LEFConfidence","Medium","Medium","Medium"]
["LEFLevel","5. CF & PoA + TCap & RS (Lowest Level)","3. TEF + TCap & RS","4. CF & PoA + Susc"]
["LEFMaximum",20,15,20]
["LEFMinimum",2,2,3]
["LEFMostLikely",10,7,8]
["LEFRationale","Successful attacks occur at an estimated 10% of attempted breaches.","Historical data suggests that a fraction of phishing attempts lead to successful unauthorized access.","Automated scanning increases exposure likelihood, though not all misconfigurations lead to a breach."]
["PLProdLConfidence","Medium","Medium","Medium"]
["PLProdLMaximum",500000,5000000,3000000]
["PLProdLMinimum",50000,250000,200000]
["PLProdLMostLikely",150000,1000000,750000]
["PLProdLRationale","Downtime impact on transaction processing.","Loss of competitive edge due to stolen R&D efforts leading to counterfeit or competing products.","Potential disruptions in customer trust and operational downtime due to regulatory audits and remediation efforts."]
["PLReplCConfidence","High","High","High"]
["PLReplCMaximum",1000000,25000000,2500000]
["PLReplCMinimum",100000,1000000,150000]
["PLReplCMostLikely",500000,7500000,900000]
["PLReplCRationale","Cost of system replacement, forensic investigations, and security upgrades.","Cost of replacing compromised intellectual property, including new product development and legal protections.","Cost of securing misconfigured cloud environments, forensic investigations, and implementing new security measures."]
["PLRespCConfidence","Medium","Medium","Medium"]
["PLRespCMaximum",750000,1000000,1500000]
["PLRespCMinimum",100000,100000,200000]
["PLRespCMostLikely",300000,400000,600000]
["PLRespCRationale","Incident response, legal fees, regulatory fines.","Incident response, legal fees, security monitoring enhancements.","Incident response, notification costs, regulatory fines, and customer compensation expenses."]
["PoAConfidence","Medium","High","High"]
["PoAMaximum",0.9,0.9,0.8]
["PoAMinimum",0.1,0.1,0.15]
["PoAMostLikely",0.4,0.5,0.45]
["PoARationale","Attack success rates from similar businesses suggest moderate likelihood.","Attackers are persistent, well-funded, and willing to take significant risks for high-value intellectual property.","Attackers frequently exploit misconfigurations, but some data may be low-value, limiting motivation."]
["RSConfidence","Medium","Medium","Medium"]
["RSMaximum",0.95,0.85,0.85]
["RSMinimum",0.2,0.2,0.3]
["RSMostLikely",0.6,0.55000000000000004,0.6]
["RSRationale","Security controls in place, but gaps exist.","Data protection measures are in place, but gaps remain in user behavior and endpoint security.","Cloud security tools offer detection capabilities, but remediation delays increase risk exposure."]
["SLEFConfidence","Low","Medium","Low"]
["SLEFMaximum",1,1,1]
["SLEFMinimum",0,0,0]
["SLEFMostLikely",0.25,0.3,0.2]
["SLEFRationale","Secondary loss frequency is uncertain due to variable regulatory responses and customer reactions.","Secondary consequences depend on media coverage and legal exposure.","Secondary loss depends on regulatory response, customer backlash, and breach severity."]
["SLMCAdvLConfidence","Medium","Medium","Medium"]
["SLMCAdvLMaximum",100000,2000000,2000000]
["SLMCAdvLMinimum",0,0,0]
["SLMCAdvLMostLikely",25000,500000,500000]
["SLMCAdvLRationale","Competitive advantage loss is moderate as some customers may switch to competitors due to trust concerns.","Competitive loss if stolen data accelerates a rival’s product launch.","Potential competitive advantage loss if sensitive business plans or intellectual property are exposed."]
["SLMFinJuConfidence","Low","Low","Low"]
["SLMFinJuMaximum",100000,500000,1500000]
["SLMFinJuMinimum",0,0,0]
["SLMFinJuMostLikely",25000,100000,300000]
["SLMFinJuRationale","Potential legal and regulatory fines depend on breach severity and jurisdictional rules.","Potential legal actions from shareholders or regulators.","Regulatory fines vary based on jurisdiction and compliance requirements."]
["SLMRepuDConfidence","Medium","Medium","Medium"]
["SLMRepuDMaximum",100000,0,2500000]
["SLMRepuDMinimum",0,0,0]
["SLMRepuDMostLikely",25000,0,600000]
["SLMRepuDRationale","Loss of customer trust and reputational damage could lead to reduced revenue, but impact varies by media exposure.","Reputation impact is not expected from a breach of non-private data.","Damage to customer trust and potential loss of business due to perceived security weaknesses."]
["SLMRespCConfidence","Medium","Medium","Medium"]
["SLMRespCMaximum",100000,750000,1000000]
["SLMRespCMinimum",0,0,0]
["SLMRespCMostLikely",25000,250000,350000]
["SLMRespCRationale","Response costs could include crisis management, PR, and increased compliance costs.","Costs of PR damage control and compliance reassessments.","Legal and PR costs to mitigate fallout and manage post-breach regulatory compliance."]
["SuscConfidence","Medium","Medium","Medium"]
["SuscMaximum",0.8,0.7,0.75]
["SuscMinimum",0.1,0.1,0.1]
["SuscMostLikely",0.4,0.4,0.4]
["SuscRationale","Security posture is moderate, but certain vectors remain vulnerable.","Employees are trained on phishing risks, but social engineering attacks remain effective.","Cloud misconfigurations are preventable but persist due to human error and weak policies."]
["TCapConfidence","High","High","High"]
["TCapMaximum",0.9,0.95,0.9]
["TCapMinimum",0.2,0.3,0.25]
["TCapMostLikely",0.7,0.75,0.7]
["TCapRationale","Attack attempts occur frequently based on historical attack data.","Threat actors have demonstrated the ability to bypass similar security controls in other organizations.","Attackers have easy access to automated tools that scan for cloud storage misconfigurations."]
["TEFConfidence","High","High","High"]
["TEFMaximum",50,40,40]
["TEFMinimum",5,5,5]
["TEFMostLikely",20,15,15]
["TEFRationale","Threat actors are sophisticated and have demonstrated success against similar organizations.","Based on historical attacks against R&D organizations in the same industry.","Cloud misconfiguration events are frequent, with regular scans increasing exposure risk."]

{"sheet":"Data Migrator","index":17,"n_rows":99,"n_cols":3,"rows_kept":99,"cut":false,"hidden":true,"merged":[]}
[]
[null,"FAIR-U Workbook for Learners\n© FAIR Institute. All rights reserved. "]
[]
["Named Range","Current Values of User Data","Migration Data"]
["AnalysisName",{"$f":"IF(AnalysisName=\"\",\"\",AnalysisName)","v":""},"Reza Khaleeli"]
["AnalysisAsset",{"$f":"IF(AnalysisAsset=\"\",\"\",AnalysisAsset)","v":""},"Material Nonpublic Information in the form of highly sensitive data, including a broad array of files related to our services and customers, all in a human intelligible form (e.g., Microsoft Word, Excel, PowerPoint files). These files are all proprietary information."]
["AnalysisCreateDate",{"$f":"IF(AnalysisCreateDate=\"\",\"\",AnalysisCreateDate)","v":""},"2025-04-03"]
["AnalysisCurrency",{"$f":"IF(AnalysisCurrency=\"\",\"\",AnalysisCurrency)","v":""},"Dollars (All Countries)"]
["AnalysisEffect",{"$f":"IF(AnalysisEffect=\"\",\"\",AnalysisEffect)","v":""},"A State sponsored threat agent maliciously gains access to, and misuses, highly sensitive OCI information about OCI Services and its customers, with the intent to use that information to target OCI customers for further attacks and exploitation for monetary gain. When this event occurs, OCI will suffer primary productivity and response losses, and OCI may also suffer secondary reputation and response costs, along with fines and judgments."]
["AnalysisMethod",{"$f":"IF(AnalysisMethod=\"\",\"\",AnalysisMethod)","v":""},"Common attack vectors include social engineering attacks, credential theft, vulnerability exploits, and insufficient protection against insider threats. "]
["AnalysisMotiveIntent",{"$f":"IF(AnalysisMotiveIntent=\"\",\"\",AnalysisMotiveIntent)","v":""},"Profitseeking"]
["AnalysisPeriodEnd",{"$f":"IF(AnalysisPeriodEnd=\"\",\"\",AnalysisPeriodEnd)","v":""},"00:00:00"]
["AnalysisPeriodStart",{"$f":"IF(AnalysisPeriodStart=\"\",\"\",AnalysisPeriodStart)","v":""},"00:00:00"]
["AnalysisPurpose",{"$f":"IF(AnalysisPurpose=\"\",\"\",AnalysisPurpose)","v":""},"Testing of workbook"]
["AnalysisThreatCapability",{"$f":"IF(AnalysisThreatCapability=\"\",\"\",AnalysisThreatCapability)","v":""},"High skilled and trained"]
["AnalysisThreatContact",{"$f":"IF(AnalysisThreatContact=\"\",\"\",AnalysisThreatContact)","v":""},"Intentional"]
["AnalysisThreatDescription",{"$f":"IF(AnalysisThreatDescription=\"\",\"\",AnalysisThreatDescription)","v":""},"A malicious actor is able to successfully identify a vulnerable system, deliver a malicious payload, exploit that system to gain a foothold into Oracle, move laterally, conduct actions on the objective, and exfiltrate data with a very low chance of being detected by existing security monitoring capabilities."]
["AnalysisThreatName",{"$f":"IF(AnalysisThreatName=\"\",\"\",AnalysisThreatName)","v":""},"Internal System Compromise"]
["AnalysisThreatRiskTolerance",{"$f":"IF(AnalysisThreatRiskTolerance=\"\",\"\",AnalysisThreatRiskTolerance)","v":""},"Imprisonment"]
["AnalysisThreatSponsorship",{"$f":"IF(AnalysisThreatSponsorship=\"\",\"\",AnalysisThreatSponsorship)","v":""},"State sponsored"]
["AnalysisThreatTargets",{"$f":"IF(AnalysisThreatTargets=\"\",\"\",AnalysisThreatTargets)","v":""},"Infrastructure assets"]
["AnalysisThreatType",{"$f":"IF(AnalysisThreatType=\"\",\"\",AnalysisThreatType)","v":""},"Malicious"]
["AnalysisUpdateDate",{"$f":"IF(AnalysisUpdateDate=\"\",\"\",AnalysisUpdateDate)","v":""},"2025-04-03"]
["CFConfidence",{"$f":"IF(CFConfidence=\"\",\"\",CFConfidence)","v":""},"High"]
["CFMaximum",{"$f":"IF(CFMaximum=\"\",\"\",CFMaximum)","v":""},12]
["CFMinimum",{"$f":"IF(CFMinimum=\"\",\"\",CFMinimum)","v":""},3]
["CFMostLikely",{"$f":"IF(CFMostLikely=\"\",\"\",CFMostLikely)","v":""},6]
["CFRationale",{"$f":"IF(CFRationale=\"\",\"\",CFRationale)","v":""},"High, at least once every other month an external actor is able to gain access to OCI systems."]
["LEFConfidence",{"$f":"IF(LEFConfidence=\"\",\"\",LEFConfidence)","v":""}]
["LEFLevel",{"$f":"IF(LEFLevel=\"\",\"\",LEFLevel)","v":"5. CF & PoA + TCap & RS (Lowest Level)"},"4. CF & PoA + Susc"]
["LEFMaximum",{"$f":"IF(LEFMaximum=\"\",\"\",LEFMaximum)","v":""}]
["LEFMinimum",{"$f":"IF(LEFMinimum=\"\",\"\",LEFMinimum)","v":""}]
["LEFMostLikely",{"$f":"IF(LEFMostLikely=\"\",\"\",LEFMostLikely)","v":""}]
["LEFRationale",{"$f":"IF(LEFRationale=\"\",\"\",LEFRationale)","v":""}]
["PLProdLConfidence",{"$f":"IF(PLProdLConfidence=\"\",\"\",PLProdLConfidence)","v":""},"High"]
["PLProdLMaximum",{"$f":"IF(PLProdLMaximum=\"\",\"\",PLProdLMaximum)","v":""},4000000]
["PLProdLMinimum",{"$f":"IF(PLProdLMinimum=\"\",\"\",PLProdLMinimum)","v":""},2600000]
["PLProdLMostLikely",{"$f":"IF(PLProdLMostLikely=\"\",\"\",PLProdLMostLikely)","v":""},3220000]
["PLProdLRationale",{"$f":"IF(PLProdLRationale=\"\",\"\",PLProdLRationale)","v":""},"20 people for 6 months= 20,800 hours (2,080 hours per person per year)\nHourly M2 employee cost = $88 "]
["PLReplCConfidence",{"$f":"IF(PLReplCConfidence=\"\",\"\",PLReplCConfidence)","v":""},"High"]
["PLReplCMaximum",{"$f":"IF(PLReplCMaximum=\"\",\"\",PLReplCMaximum)","v":""},15000000]
["PLReplCMinimum",{"$f":"IF(PLReplCMinimum=\"\",\"\",PLReplCMinimum)","v":""},5000000]
["PLReplCMostLikely",{"$f":"IF(PLReplCMostLikely=\"\",\"\",PLReplCMostLikely)","v":""},10000000]
["PLReplCRationale",{"$f":"IF(PLReplCRationale=\"\",\"\",PLReplCRationale)","v":""},"2022 IBM Cost of Data Breach Report"]
["PLRespCConfidence",{"$f":"IF(PLRespCConfidence=\"\",\"\",PLRespCConfidence)","v":""},"High"]
["PLRespCMaximum",{"$f":"IF(PLRespCMaximum=\"\",\"\",PLRespCMaximum)","v":""},2100000]
["PLRespCMinimum",{"$f":"IF(PLRespCMinimum=\"\",\"\",PLRespCMinimum)","v":""},700000]
["PLRespCMostLikely",{"$f":"IF(PLRespCMostLikely=\"\",\"\",PLRespCMostLikely)","v":""},1400000]
["PLRespCRationale",{"$f":"IF(PLRespCRationale=\"\",\"\",PLRespCRationale)","v":""},"2022 IBM Cost of Data Breach Report"]
["PoAConfidence",{"$f":"IF(PoAConfidence=\"\",\"\",PoAConfidence)","v":""},"High"]
["PoAMaximum",{"$f":"IF(PoAMaximum=\"\",\"\",PoAMaximum)","v":""},0.95]
["PoAMinimum",{"$f":"IF(PoAMinimum=\"\",\"\",PoAMinimum)","v":""},0.6]
["PoAMostLikely",{"$f":"IF(PoAMostLikely=\"\",\"\",PoAMostLikely)","v":""},0.8]
["PoARationale",{"$f":"IF(PoARationale=\"\",\"\",PoARationale)","v":""},"Very High, as a State sponsored actor is extremely likely to act against the Asset, especially if contact is made."]
["RSConfidence",{"$f":"IF(RSConfidence=\"\",\"\",RSConfidence)","v":""},"High"]
["RSMaximum",{"$f":"IF(RSMaximum=\"\",\"\",RSMaximum)","v":""},0.04]
["RSMinimum",{"$f":"IF(RSMinimum=\"\",\"\",RSMinimum)","v":""},0.01]
["RSMostLikely",{"$f":"IF(RSMostLikely=\"\",\"\",RSMostLikely)","v":""},0.02]
["RSRationale",{"$f":"IF(RSRationale=\"\",\"\",RSRationale)","v":""},"The current Resistance Strength to detect actors is Very Low (weak), when compared against the threat capability (bottom 2%)."]
["SLEFConfidence",{"$f":"IF(SLEFConfidence=\"\",\"\",SLEFConfidence)","v":""},"High"]
["SLEFMaximum",{"$f":"IF(SLEFMaximum=\"\",\"\",SLEFMaximum)","v":""},0.99]
["SLEFMinimum",{"$f":"IF(SLEFMinimum=\"\",\"\",SLEFMinimum)","v":""},0.9]
["SLEFMostLikely",{"$f":"IF(SLEFMostLikely=\"\",\"\",SLEFMostLikely)","v":""},0.95]
["SLEFRationale",{"$f":"IF(SLEFRationale=\"\",\"\",SLEFRationale)","v":""},"Since this event involves the compromise of Material Nonpublic Information, it is highly likely that one or more of the Secondary Stakeholder communities would be required to be informed and must be “managed”. Consequently, the probability of Secondary Loss Events occurring is Very High (90% to 100% probability of occurring)."]
["SLMCAdvLConfidence",{"$f":"IF(SLMCAdvLConfidence=\"\",\"\",SLMCAdvLConfidence)","v":""},"High"]
["SLMCAdvLMaximum",{"$f":"IF(SLMCAdvLMaximum=\"\",\"\",SLMCAdvLMaximum)","v":""},50000000]
["SLMCAdvLMinimum",{"$f":"IF(SLMCAdvLMinimum=\"\",\"\",SLMCAdvLMinimum)","v":""},20000000]
["SLMCAdvLMostLikely",{"$f":"IF(SLMCAdvLMostLikely=\"\",\"\",SLMCAdvLMostLikely)","v":""},35000000]
["SLMCAdvLRationale",{"$f":"IF(SLMCAdvLRationale=\"\",\"\",SLMCAdvLRationale)","v":""},"2022 IBM Cost of Data Breach Report"]
["SLMFinJuConfidence",{"$f":"IF(SLMFinJuConfidence=\"\",\"\",SLMFinJuConfidence)","v":""},"High"]
["SLMFinJuMaximum",{"$f":"IF(SLMFinJuMaximum=\"\",\"\",SLMFinJuMaximum)","v":""},95000000]
["SLMFinJuMinimum",{"$f":"IF(SLMFinJuMinimum=\"\",\"\",SLMFinJuMinimum)","v":""},75000000]
["SLMFinJuMostLikely",{"$f":"IF(SLMFinJuMostLikely=\"\",\"\",SLMFinJuMostLikely)","v":""},85000000]
["SLMFinJuRationale",{"$f":"IF(SLMFinJuRationale=\"\",\"\",SLMFinJuRationale)","v":""},"Used data from the AWS/CapitalOne Cloud data breach"]
["SLMRepuDConfidence",{"$f":"IF(SLMRepuDConfidence=\"\",\"\",SLMRepuDConfidence)","v":""},"High"]
["SLMRepuDMaximum",{"$f":"IF(SLMRepuDMaximum=\"\",\"\",SLMRepuDMaximum)","v":""},3500000]
["SLMRepuDMinimum",{"$f":"IF(SLMRepuDMinimum=\"\",\"\",SLMRepuDMinimum)","v":""},1100000]
["SLMRepuDMostLikely",{"$f":"IF(SLMRepuDMostLikely=\"\",\"\",SLMRepuDMostLikely)","v":""},2200000]
["SLMRepuDRationale",{"$f":"IF(SLMRepuDRationale=\"\",\"\",SLMRepuDRationale)","v":""},"2022 IBM Cost of Data Breach Report"]
["SLMRespCConfidence",{"$f":"IF(SLMRespCConfidence=\"\",\"\",SLMRespCConfidence)","v":""},"High"]
["SLMRespCMaximum",{"$f":"IF(SLMRespCMaximum=\"\",\"\",SLMRespCMaximum)","v":""},1200000]
["SLMRespCMinimum",{"$f":"IF(SLMRespCMinimum=\"\",\"\",SLMRespCMinimum)","v":""},500000]
["SLMRespCMostLikely",{"$f":"IF(SLMRespCMostLikely=\"\",\"\",SLMRespCMostLikely)","v":""},800000]
["SLMRespCRationale",{"$f":"IF(SLMRespCRationale=\"\",\"\",SLMRespCRationale)","v":""},"2022 IBM Cost of Data Breach Report"]
["SuscConfidence",{"$f":"IF(SuscConfidence=\"\",\"\",SuscConfidence)","v":""}]
["SuscMaximum",{"$f":"IF(SuscMaximum=\"\",\"\",SuscMaximum)","v":""}]
["SuscMinimum",{"$f":"IF(SuscMinimum=\"\",\"\",SuscMinimum)","v":""}]
["SuscMostLikely",{"$f":"IF(SuscMostLikely=\"\",\"\",SuscMostLikely)","v":""}]
["SuscRationale",{"$f":"IF(SuscRationale=\"\",\"\",SuscRationale)","v":""}]
["TCapConfidence",{"$f":"IF(TCapConfidence=\"\",\"\",TCapConfidence)","v":""},"High"]
["TCapMaximum",{"$f":"IF(TCapMaximum=\"\",\"\",TCapMaximum)","v":""},0.99]
["TCapMinimum",{"$f":"IF(TCapMinimum=\"\",\"\",TCapMinimum)","v":""},0.95]
["TCapMostLikely",{"$f":"IF(TCapMostLikely=\"\",\"\",TCapMostLikely)","v":""},0.97]
["TCapRationale",{"$f":"IF(TCapRationale=\"\",\"\",TCapRationale)","v":""},"The Threat Capability of a State sponsored actor can be estimated to be Very High (expert skills and resources), when compared against the overall threat population (top 2%)."]
["TEFConfidence",{"$f":"IF(TEFConfidence=\"\",\"\",TEFConfidence)","v":""}]
["TEFMaximum",{"$f":"IF(TEFMaximum=\"\",\"\",TEFMaximum)","v":""}]
["TEFMinimum",{"$f":"IF(TEFMinimum=\"\",\"\",TEFMinimum)","v":""}]
["TEFMostLikely",{"$f":"IF(TEFMostLikely=\"\",\"\",TEFMostLikely)","v":""}]
["TEFRationale",{"$f":"IF(TEFRationale=\"\",\"\",TEFRationale)","v":""}]