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["AI Calculations/Steps ->","Base Script","You are an AI monte carlo risk register model builder built into EXCEL. Answer only in the specified format so that it can be parsed.",null,null,null,"AI Creativity","Enter a value between 0 and 1 that represents how random the responses should be. Lower values mean more precise and predictable text generation."]
[null,"Inputs",{"$f":"\"The user has inputted\\n{\"&_xlfn.TEXTJOIN(\",\\n\",1,Table1[Category]&\" : \"&Table1[Input])&\"}.\"","v":"The user has inputted\\n{Organization Profile : Generate cybersecurity specific risks to a health insurance company.,\\nRisk Scope : }."},null,null,null,0]
[null,"Summary",{"$f":"\"You have already generated the following summary of the user inputs and context \\n\\n\"&Summary&\"\\nThis summary should serve as the foundation for your responses.\\n\"","v":"You have already generated the following summary of the user inputs and context \\n\\n{Quantitative Specifications: \r\n- Annual Revenue: $500 million\r\n- Current Cybersecurity Budget: $5 million\r\n- Estimated Annual Cybersecurity Incidents: 10\r\n- Average Cost per Incident: $1 million\r\n- Current Risk Mitigation Level: Moderate\r\n- Industry Average Cybersecurity Maturity Level: High\r\n- Potential Control Investment Increase: $2 million}\r\n\r\nThe health insurance company faces significant cybersecurity risks, with a quantified non-diversifiable tail risk that could potentially impact its revenue. The likelihood of cybersecurity incidents exceeding 1% of revenue in a single year is approximately 30%, 2% is 20%, 5% is 10%, 10% is 5%, and 20% is 1%. The top five risk categories include data breaches, ransomware attacks, insider threats, third-party vendor risks, and phishing attacks. Data breaches have a 25% likelihood of exceeding 1% of revenue, ransomware attacks 20%, insider threats 15%, third-party vendor risks 10%, and phishing attacks 5%. With targeted control investments, the maximum percent reduction of probability-weighted losses could be achieved as follows: data breaches (40%), ransomware attacks (35%), insider threats (30%), third-party vendor risks (25%), and phishing attacks (20%). These estimates suggest that strategic investments in cybersecurity controls, such as advanced threat detection systems, employee training programs, and enhanced vendor management protocols, could significantly mitigate potential losses.\\nThis summary should serve as the foundation for your responses.\\n"},{"$f":"AI_WITH_FALLBACK(Base_Script&UserInputs&\"\\nBased on user inputs and feedback, generate a high level realistic summary of the organization's quantified non-diversifiable tail risk within the context specified\"&\", including the likelihood of exceeding 1%, 2%, 5%, 10%, or 20% of revenue in a single year. Include similar estimates broken down by top 5 risk categories.\"& \"Include estimates regarding the maximum percent reduction of probability weighted losses within each category that could be realistically achieved with control investments that are not likely to be in place already given the inputs. Return as paragraph.\"&\"\\nStart with a quick list of explicit quantitative specifications that can be used to anchor estimates. If user didn't provide quantitative details, generate unified hypothetical.\"&\" Hypothetical should be both unified and realistic. You should aim for a level of realism such that there is minimal likelihood of harm if the user blindly takes your generation as fact\",Temp,4096,model_name)","v":"{Quantitative Specifications: \r\n- Annual Revenue: $500 million\r\n- Current Cybersecurity Budget: $5 million\r\n- Estimated Annual Cybersecurity Incidents: 10\r\n- Average Cost per Incident: $1 million\r\n- Current Risk Mitigation Level: Moderate\r\n- Industry Average Cybersecurity Maturity Level: High\r\n- Potential Control Investment Increase: $2 million}\r\n\r\nThe health insurance company faces significant cybersecurity risks, with a quantified non-diversifiable tail risk that could potentially impact its revenue. The likelihood of cybersecurity incidents exceeding 1% of revenue in a single year is approximately 30%, 2% is 20%, 5% is 10%, 10% is 5%, and 20% is 1%. The top five risk categories include data breaches, ransomware attacks, insider threats, third-party vendor risks, and phishing attacks. Data breaches have a 25% likelihood of exceeding 1% of revenue, ransomware attacks 20%, insider threats 15%, third-party vendor risks 10%, and phishing attacks 5%. With targeted control investments, the maximum percent reduction of probability-weighted losses could be achieved as follows: data breaches (40%), ransomware attacks (35%), insider threats (30%), third-party vendor risks (25%), and phishing attacks (20%). These estimates suggest that strategic investments in cybersecurity controls, such as advanced threat detection systems, employee training programs, and enhanced vendor management protocols, could significantly mitigate potential losses."}]
[null,{"$f":"AI_WITH_FALLBACK(Base_Script&Summary_Input&\"\\nBased on these inputs return just a ::: delimited column of EXACTLY \"&Max_Risks&\" realistic tail risk event descriptions that are specific to inputs, non diversifiable (i.e. rare and impactful), unique and mutually exclusive, unambiguous and precise in definition and scale, informative, and self-containted. \"&\"\\nRisk events must be ordered and grouped based on risk event category. Do not include any indexing. Entries must be in *[a-Z]* form with ::: between each entry. Do not directly include probability or monetary impact. Do not start with :::.\",Temp,4096,model_name)","v":"Data breach resulting in unauthorized access to 1 million patient records ::: Ransomware attack causing a week-long shutdown of critical systems ::: Insider threat leading to the theft of proprietary algorithms ::: Third-party vendor data leak exposing sensitive client information ::: Phishing attack compromising executive email accounts ::: Data breach involving the loss of encrypted financial data ::: Ransomware attack demanding cryptocurrency payment to restore operations ::: Insider threat facilitating unauthorized financial transactions ::: Third-party vendor system failure disrupting service delivery ::: Phishing attack resulting in fraudulent wire transfers"},null,null,{"$f":"IF(LEN(TRIM(Feedback_on_Risk_Names))>0,\nAI_WITH_FALLBACK(Base_Script&Summary_Input&\"\\nBased on these inputs return just a ::: delimited column of 1 to \"&Max_Risks&\" realistic tail risk event descriptions that are specific to inputs, non diversifiable (i.e. rare and impactful), unique and mutually exclusive, unambiguous and precise in definition and scale, informative, and self-containted. \"&\"\\nRisk events must be ordered and grouped based on risk event category. Do not include any indexing. Entries must be in *[a-Z]* form with ::: between each entry. Do not directly include probability or monetary impact. Do not start with :::.\"&\"\\nPrevious Generation to Fix: \\n\"&AI_Description_Output&\"\\nFeedback from User: \\n\"&Feedback_on_Risk_Names,Temp,4096,model_name),AI_Description_Output)","v":"Data breach resulting in unauthorized access to 1 million patient records ::: Ransomware attack causing a week-long shutdown of critical systems ::: Insider threat leading to the theft of proprietary algorithms ::: Third-party vendor data leak exposing sensitive client information ::: Phishing attack compromising executive email accounts ::: Data breach involving the loss of encrypted financial data ::: Ransomware attack demanding cryptocurrency payment to restore operations ::: Insider threat facilitating unauthorized financial transactions ::: Third-party vendor system failure disrupting service delivery ::: Phishing attack resulting in fraudulent wire transfers"}]
[null,"Risk Name Input",{"$f":"\"You have already generated the following tail risk event names\\n{\"&_xlfn.TEXTJOIN(\",\\n\",1,Risk_Names)&\"}.\\n\"","v":"You have already generated the following tail risk event names\\n{data breach involving unauthorized access to sensitive customer information leading to regulatory fines and reputational damage,\\nRansomware attack causing a week-long shutdown of critical systems,\\nInsider threat leading to the theft of proprietary algorithms,\\nThird-party vendor data leak exposing sensitive client information,\\nPhishing attack compromising executive email accounts,\\nData breach involving the loss of encrypted financial data,\\nRansomware attack demanding cryptocurrency payment to restore operations,\\nInsider threat facilitating unauthorized financial transactions,\\nThird-party vendor system failure disrupting service delivery,\\nPhishing attack resulting in fraudulent wire transfers}.\\n"}]
[null,{"$f":"AI_WITH_FALLBACK(Base_Script&Summary_Input&Risk_Name_Text&\"Based on these names, return just a ::: delimited column of broader risk categories for each of these \"&SUMPRODUCT(1*(LEN(Risk_Names)>0))&\" non-diversifiable tail risk events pre-ordered by similarly. Categories must be specific to inputs, precise, unambiguous, and informative for both the USER and your future generation tasks.\"&\" Do not include risk name or index. Delimit with :::. Do not start with :::\",Temp,4096,model_name)","v":"Data Breaches:::Ransomware Attacks:::Insider Threats:::Third-Party Vendor Risks:::Phishing Attacks:::Data Breaches:::Ransomware Attacks:::Insider Threats:::Third-Party Vendor Risks:::Phishing Attacks"},null,null,{"$f":"IF(LEN(TRIM(Feedback_on_Categories))>0,\nAI_WITH_FALLBACK(Base_Script&Summary_Input&Risk_Name_Text&\"Based on these names, return just a ::: delimited column of broader risk categories for each of these \"&SUMPRODUCT(1*(LEN(Risk_Names)>0))&\" non-diversifiable tail risk events pre-ordered by similarly. Categories must be specific to inputs, precise, unambiguous, and informative for both the USER and your future generation tasks.\"&\" Do not include risk name or index. Delimit with :::. Do not start with :::\"&\"\\nPrevious Generation to Fix: \\n\"&AI_Risk_Tags&\"\\nFeedback from User: \\n\"&Feedback_on_Categories,Temp,4096,model_name),AI_Risk_Tags)","v":"Data Breaches:::Ransomware Attacks:::Insider Threats:::Third-Party Vendor Risks:::Phishing Attacks:::Data Breaches:::Ransomware Attacks:::Insider Threats:::Third-Party Vendor Risks:::Phishing Attacks"}]
[null,"Risk Name and Tag Inputs",{"$f":"\"You have already generated the following risk names and tags\\n{\"&_xlfn.TEXTJOIN(\",\\n\",1,Risk_Names&\" : \"&Risk_Tags)&\"}\\n.\"","v":"You have already generated the following risk names and tags\\n{data breach involving unauthorized access to sensitive customer information leading to regulatory fines and reputational damage : Data Breach Risk,\\nRansomware attack causing a week-long shutdown of critical systems : Ransomware Attacks,\\nInsider threat leading to the theft of proprietary algorithms : Insider Threats,\\nThird-party vendor data leak exposing sensitive client information : Third-Party Vendor Risks,\\nPhishing attack compromising executive email accounts : Phishing Attacks,\\nData breach involving the loss of encrypted financial data : Data Breaches,\\nRansomware attack demanding cryptocurrency payment to restore operations : Ransomware Attacks,\\nInsider threat facilitating unauthorized financial transactions : Insider Threats,\\nThird-party vendor system failure disrupting service delivery : Third-Party Vendor Risks,\\nPhishing attack resulting in fraudulent wire transfers : Phishing Attacks,\\n : ,\\n : ,\\n : ,\\n : ,\\n : ,\\n : ,\\n : ,\\n : ,\\n : ,\\n : }\\n."}]
[null,{"$f":"TRIM(AI_WITH_FALLBACK(Base_Script&Summary_Input&RiskName_Tags&\"\\nReturn just a ::: delimited column of informed annual probabilities between 0 and 1 for each of these \"&SUMPRODUCT(1*(LEN(Risk_Names)>0))&\" tail risk events. \"&\"Risk events (even those categorized together) are assumed to be independent. \" &\"Do not start with :::. Do not include whitespace or line breaks.\",0,4096,model_name))","v":"0.25:::0.20:::0.15:::0.10:::0.05:::0.25:::0.20:::0.15:::0.10:::0.05"},null,null,{"$f":"IF(LEN(TRIM(Feedback_on_Probabilities))>0,\nTRIM(AI_WITH_FALLBACK(Base_Script&Summary_Input&RiskName_Tags&\"\\nReturn just a ::: delimited column of informed annual probabilities between 0 and 1 for each of these \"& SUMPRODUCT(1*(LEN(Risk_Names)>0))&\" tail risk events. \"&\"Risk events (even those categorized together) are assumed to be independent. \"&\"Do not start with :::. Do not include whitespace or line breaks\"&\"\\nPrevious Generation to Fix: \\n\"&AI_Risk_Probabilities&\"\\nFeedback from User: \\n\"&Feedback_on_Probabilities,0,4096,model_name)),AI_Risk_Probabilities)","v":"0.25:::0.20:::0.15:::0.10:::0.05:::0.25:::0.20:::0.15:::0.10:::0.05"}]
[null,"Risk Name Tag P",{"$f":"\"You have already generated the following risks\\n{\"&_xlfn.TEXTJOIN(\",\\n\",1,\"{Name:\"&Risk_Names&\",Tag:\"&Risk_Tags&\",AnnualProbability:\"&Risk_Probabilities&\"}\")&\"}.\\n\"","v":"You have already generated the following risks\\n{{Name:data breach involving unauthorized access to sensitive customer information leading to regulatory fines and reputational damage,Tag:Data Breach Risk,AnnualProbability:0.15},\\n{Name:Ransomware attack causing a week-long shutdown of critical systems,Tag:Ransomware Attacks,AnnualProbability:0.2},\\n{Name:Insider threat leading to the theft of proprietary algorithms,Tag:Insider Threats,AnnualProbability:0.15},\\n{Name:Third-party vendor data leak exposing sensitive client information,Tag:Third-Party Vendor Risks,AnnualProbability:0.1},\\n{Name:Phishing attack compromising executive email accounts,Tag:Phishing Attacks,AnnualProbability:0.05},\\n{Name:Data breach involving the loss of encrypted financial data,Tag:Data Breaches,AnnualProbability:0.25},\\n{Name:Ransomware attack demanding cryptocurrency payment to restore operations,Tag:Ransomware Attacks,AnnualProbability:0.2},\\n{Name:Insider threat facilitating unauthorized financial transactions,Tag:Insider Threats,AnnualProbability:0.15},\\n{Name:Third-party vendor system failure disrupting service delivery,Tag:Third-Party Vendor Risks,AnnualProbability:0.1},\\n{Name:Phishing attack resulting in fraudulent wire transfers,Tag:Phishing Attacks,AnnualProbability:0.05},\\n{Name:,Tag:,AnnualProbability:},\\n{Name:,Tag:,AnnualProbability:},\\n{Name:,Tag:,AnnualProbability:},\\n{Name:,Tag:,AnnualProbability:},\\n{Name:,Tag:,AnnualProbability:},\\n{Name:,Tag:,AnnualProbability:},\\n{Name:,Tag:,AnnualProbability:},\\n{Name:,Tag:,AnnualProbability:},\\n{Name:,Tag:,AnnualProbability:},\\n{Name:,Tag:,AnnualProbability:}}.\\n"}]
[null,"Risk UB LB Query",{"$f":"\"Return just an unquoted table of informed LB (p5) and UB (p95) for the potential lognormal annual impact for each of these \"&SUMPRODUCT(1*(LEN(Risk_Names)>0))&\" non-diversifiable risks given occurrence. Each entry must be exactly: Risk#|LB:$*[0-9]*[T,B,M,K]|UB:$*[0-9]*[T,B,M,K] with ::: between each entry. The size of impacts must align with Organization Size and Type and Risk Definitions\"","v":"Return just an unquoted table of informed LB (p5) and UB (p95) for the potential lognormal annual impact for each of these 10 non-diversifiable risks given occurrence. Each entry must be exactly: Risk#|LB:$*[0-9]*[T,B,M,K]|UB:$*[0-9]*[T,B,M,K] with ::: between each entry. The size of impacts must align with Organization Size and Type and Risk Definitions"}]
[null,{"$f":"AI_WITH_FALLBACK(Base_Script&Summary_Input&Risk_Name_Tag_P&Risk_UB_LB_Query,Temp,4096,model_name)","v":"Risk1|LB:$500K|UB:$5M:::Risk2|LB:$1M|UB:$10M:::Risk3|LB:$750K|UB:$7.5M:::Risk4|LB:$500K|UB:$5M:::Risk5|LB:$250K|UB:$2.5M:::Risk6|LB:$1M|UB:$10M:::Risk7|LB:$1M|UB:$10M:::Risk8|LB:$750K|UB:$7.5M:::Risk9|LB:$500K|UB:$5M:::Risk10|LB:$250K|UB:$2.5M"},null,null,{"$f":"IF(LEN(TRIM(Feedback_on_Impact))>0,AI_WITH_FALLBACK(Base_Script&Summary_Input&Risk_Name_Tag_P&Risk_UB_LB_Query&\"\\nPrevious Generation to Fix: \\n\"&AI_p5_p95&\"\\nFeedback from User: \\n\"&Feedback_on_Impact,Temp,4096,model_name),AI_p5_p95)","v":"Risk1|LB:$500K|UB:$5M:::Risk2|LB:$1M|UB:$10M:::Risk3|LB:$750K|UB:$7.5M:::Risk4|LB:$500K|UB:$5M:::Risk5|LB:$250K|UB:$2.5M:::Risk6|LB:$1M|UB:$10M:::Risk7|LB:$1M|UB:$10M:::Risk8|LB:$750K|UB:$7.5M:::Risk9|LB:$500K|UB:$5M:::Risk10|LB:$250K|UB:$2.5M"}]
[null,{"$f":"_xlfn.LET(\n        _xlpm.stripped, SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(AI_IMPACT_CORRECTED,\" \",\"\"),CHAR(10),\"\"),CHAR(13),\"\"),CHAR(9),\"\"),\n        _xlpm.entries,  _xlfn.TEXTSPLIT(_xlpm.stripped,,\":::\"),\n        _xlpm.toValue,  _xlfn.LAMBDA(_xlpm.txt,\n                   _xlfn.LET(\n                       _xlpm.u,    RIGHT(_xlpm.txt,1),\n                       _xlpm.mult, _xlfn.SWITCH(_xlpm.u,\"K\",1000,\"M\",1000000,\"B\",1000000000,\"T\",1000000000000,1),\n                       _xlpm.num,  VALUE(LEFT(_xlpm.txt,LEN(_xlpm.txt)-1)),\n                       _xlpm.num*_xlpm.mult\n                   )),\n        _xlpm.lbs, _xlfn.MAP(_xlpm.entries, _xlfn.LAMBDA(_xlpm.e, _xlpm.toValue(_xlfn.TEXTBEFORE(_xlfn.TEXTAFTER(_xlpm.e,\"LB:$\"),\"|\")))),\n        _xlpm.ubs, _xlfn.MAP(_xlpm.entries, _xlfn.LAMBDA(_xlpm.e, _xlpm.toValue(_xlfn.TEXTAFTER(_xlpm.e,\"UB:$\")))),\n        _xlfn.HSTACK(_xlpm.lbs, _xlpm.ubs)\n    )","v":500000},5000000]
[null,1000000,10000000]
[null,750000,7500000]
[null,500000,5000000]
[null,250000,2500000]
[null,1000000,10000000]
[null,1000000,10000000]
[null,750000,7500000]
[null,500000,5000000]
[null,250000,2500000]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[null,"Full Risk Definition",{"$f":"\"You have already generated the following risks\\n{\"&_xlfn.TEXTJOIN(\",\\n\",1,\"{Name:\"&Risk_Names&\",Tag:\"&Risk_Tags&\",AnnualProbability:\"&Risk_Probabilities&\",Potential Annual Impact p5:\"&Risk_p5&\",Potential Annual Impact p95:\"&Risk_P95&\"}\")&\"}.\\n\"","v":"You have already generated the following risks\\n{{Name:data breach involving unauthorized access to sensitive customer information leading to regulatory fines and reputational damage,Tag:Data Breach Risk,AnnualProbability:0.15,Potential Annual Impact p5:7500000,Potential Annual Impact p95:15000000},\\n{Name:Ransomware attack causing a week-long shutdown of critical systems,Tag:Ransomware Attacks,AnnualProbability:0.2,Potential Annual Impact p5:1000000,Potential Annual Impact p95:10000000},\\n{Name:Insider threat leading to the theft of proprietary algorithms,Tag:Insider Threats,AnnualProbability:0.15,Potential Annual Impact p5:750000,Potential Annual Impact p95:7500000},\\n{Name:Third-party vendor data leak exposing sensitive client information,Tag:Third-Party Vendor Risks,AnnualProbability:0.1,Potential Annual Impact p5:500000,Potential Annual Impact p95:5000000},\\n{Name:Phishing attack compromising executive email accounts,Tag:Phishing Attacks,AnnualProbability:0.05,Potential Annual Impact p5:250000,Potential Annual Impact p95:2500000},\\n{Name:Data breach involving the loss of encrypted financial data,Tag:Data Breaches,AnnualProbability:0.25,Potential Annual Impact p5:1000000,Potential Annual Impact p95:10000000},\\n{Name:Ransomware attack demanding cryptocurrency payment to restore operations,Tag:Ransomware Attacks,AnnualProbability:0.2,Potential Annual Impact p5:1000000,Potential Annual Impact p95:10000000},\\n{Name:Insider threat facilitating unauthorized financial transactions,Tag:Insider Threats,AnnualProbability:0.15,Potential Annual Impact p5:750000,Potential Annual Impact p95:7500000},\\n{Name:Third-party vendor system failure disrupting service delivery,Tag:Third-Party Vendor Risks,AnnualProbability:0.1,Potential Annual Impact p5:500000,Potential Annual Impact p95:5000000},\\n{Name:Phishing attack resulting in fraudulent wire transfers,Tag:Phishing Attacks,AnnualProbability:0.05,Potential Annual Impact p5:250000,Potential Annual Impact p95:2500000},\\n{Name:,Tag:,AnnualProbability:,Potential Annual Impact p5:,Potential Annual Impact p95:},\\n{Name:,Tag:,AnnualProbability:,Potential Annual Impact p5:,Potential Annual Impact p95:},\\n{Name:,Tag:,AnnualProbability:,Potential Annual Impact p5:,Potential Annual Impact p95:},\\n{Name:,Tag:,AnnualProbability:,Potential Annual Impact p5:,Potential Annual Impact p95:},\\n{Name:,Tag:,AnnualProbability:,Potential Annual Impact p5:,Potential Annual Impact p95:},\\n{Name:,Tag:,AnnualProbability:,Potential Annual Impact p5:,Potential Annual Impact p95:},\\n{Name:,Tag:,AnnualProbability:,Potential Annual Impact p5:,Potential Annual Impact p95:},\\n{Name:,Tag:,AnnualProbability:,Potential Annual Impact p5:,Potential Annual Impact p95:},\\n{Name:,Tag:,AnnualProbability:,Potential Annual Impact p5:,Potential Annual Impact p95:},\\n{Name:,Tag:,AnnualProbability:,Potential Annual Impact p5:,Potential Annual Impact p95:}}.\\n"}]
[null,"Control Query",{"$f":"\"Return just an unquoted table of specific, actionable, unambiguous probability or impact reducing controls for each of these \"&SUMPRODUCT(1*(LEN(Risk_Names)>0))&\" non-diversifiable tail risk events. Each entry must be exactly: Risk[0-9]*|ControlDescription:*[A-z ]* with ::: between each row and | between each column.\" &\"Do not include broad controls that are almost guaranteed to be in place already.\"&\"\\n Controls should be unique at the risk event level (no repeated control program names). \"&\"There should be exactly one control for each of the \"&SUMPRODUCT(1*(LEN(Risk_Names)>0))&\" Risk Events.\"","v":"Return just an unquoted table of specific, actionable, unambiguous probability or impact reducing controls for each of these 10 non-diversifiable tail risk events. Each entry must be exactly: Risk[0-9]*|ControlDescription:*[A-z ]* with ::: between each row and | between each column.Do not include broad controls that are almost guaranteed to be in place already.\\n Controls should be unique at the risk event level (no repeated control program names). There should be exactly one control for each of the 10 Risk Events."},null,{"$f":"\"Return just an unquoted table of annual costs for these specific, unambiguous controls on each of these \"&SUMPRODUCT(1*(LEN(Risk_Names)>0))&\" non-diversifiable tail risk events. Each entry must be exactly: Risk[0-9]*|ControlName:*[A-z ]*|ControlCost:$*[0-9]*[T,B,M,K]| with ::: between each row and | between each column.\"&\" The cost must align with Risks, Likelihood, & Severity.\"","v":"Return just an unquoted table of annual costs for these specific, unambiguous controls on each of these 10 non-diversifiable tail risk events. Each entry must be exactly: Risk[0-9]*|ControlName:*[A-z ]*|ControlCost:$*[0-9]*[T,B,M,K]| with ::: between each row and | between each column. The cost must align with Risks, Likelihood, & Severity."},{"$f":"\"Return just an unquoted table of risk reduction (combination of probability and impact reduction) percentages for these specific, unambiguous controls on each of these \"&SUMPRODUCT(1*(LEN(Risk_Names)>0))&\" non-diversifiable tail risk events. Each entry must be exactly: Risk[0-9]*|ControlName:*[A-z ]*|ControlCost:$*[0-9]*[T,B,M,K]|ControlEffectiveness:0.[0-9]* with ::: between each row and | between each column.\"&\" The effectiveness must align with Risks, Likelihood, & Severity and Control Costs.\"","v":"Return just an unquoted table of risk reduction (combination of probability and impact reduction) percentages for these specific, unambiguous controls on each of these 10 non-diversifiable tail risk events. Each entry must be exactly: Risk[0-9]*|ControlName:*[A-z ]*|ControlCost:$*[0-9]*[T,B,M,K]|ControlEffectiveness:0.[0-9]* with ::: between each row and | between each column. The effectiveness must align with Risks, Likelihood, & Severity and Control Costs."}]
[null,{"$f":"AI_WITH_FALLBACK(Base_Script&Summary_Input&Full_risk_definition&Control_Query,Temp,4096,model_name)","v":"Risk0|ControlDescription:Implement advanced data encryption and access monitoring systems:::Risk1|ControlDescription:Deploy a robust incident response plan with regular ransomware attack simulations:::Risk2|ControlDescription:Establish a comprehensive insider threat detection program with behavioral analytics:::Risk3|ControlDescription:Conduct thorough security audits and compliance checks for all third-party vendors:::Risk4|ControlDescription:Implement multi-factor authentication and regular phishing simulation training for executives:::Risk5|ControlDescription:Enhance encryption protocols and conduct regular penetration testing:::Risk6|ControlDescription:Develop a cryptocurrency payment response strategy and backup data recovery plan:::Risk7|ControlDescription:Introduce mandatory financial transaction monitoring and anomaly detection systems:::Risk8|ControlDescription:Create a vendor risk management framework with contingency planning:::Risk9|ControlDescription:Establish a fraud detection system with real-time transaction monitoring"},null,null,{"$f":"AI_WITH_FALLBACK(Base_Script&Summary_Input&Full_risk_definition&\"\\nGenerated list of controls: \\n\"&_xlfn.TEXTJOIN(\":::\",TRUE,'Risk Estimates'!A12:A32&\"|\"&'Risk Estimates'!G12:G32)&\"\\n\"&ControlQuery_2,Temp,4096,model_name)","v":"Risk1|Control Name:Implement multi-factor authentication for sensitive data access|Control Cost:$500K:::Risk2|Control Name:Deploy a robust incident response plan with regular ransomware attack simulations|Control Cost:$750K:::Risk3|Control Name:Establish a comprehensive insider threat detection program with behavioral analytics|Control Cost:$600K:::Risk4|Control Name:Conduct thorough security audits and compliance checks for all third-party vendors|Control Cost:$400K:::Risk5|Control Name:Implement multi-factor authentication and regular phishing simulation training for executives|Control Cost:$300K:::Risk6|Control Name:Enhance encryption protocols and conduct regular penetration testing|Control Cost:$550K:::Risk7|Control Name:Develop a cryptocurrency payment response strategy and backup data recovery plan|Control Cost:$700K:::Risk8|Control Name:Introduce mandatory financial transaction monitoring and anomaly detection systems|Control Cost:$650K:::Risk9|Control Name:Create a vendor risk management framework with contingency planning|Control Cost:$450K:::Risk10|Control Name:Establish a fraud detection system with real-time transaction monitoring|Control Cost:$350K"},{"$f":"AI_WITH_FALLBACK(Base_Script&Summary_Input&Full_risk_definition&\"\\nGenerated list of controls: \\n\"&_xlfn.TEXTJOIN(\":::\",TRUE,'Risk Estimates'!A12:A32&\"|\"&'Risk Estimates'!G12:G32&\"|\")&\"\\n\"&Control_Prompt_3,Temp,4096,model_name)","v":"Risk1|ControlName:Implement multi-factor authentication for sensitive data access|ControlCost:$500K|ControlEffectiveness:0.30:::Risk2|ControlName:Deploy a robust incident response plan with regular ransomware attack simulations|ControlCost:$750K|ControlEffectiveness:0.25:::Risk3|ControlName:Establish a comprehensive insider threat detection program with behavioral analytics|ControlCost:$600K|ControlEffectiveness:0.20:::Risk4|ControlName:Conduct thorough security audits and compliance checks for all third-party vendors|ControlCost:$400K|ControlEffectiveness:0.15:::Risk5|ControlName:Implement multi-factor authentication and regular phishing simulation training for executives|ControlCost:$300K|ControlEffectiveness:0.10:::Risk6|ControlName:Enhance encryption protocols and conduct regular penetration testing|ControlCost:$500K|ControlEffectiveness:0.35:::Risk7|ControlName:Develop a cryptocurrency payment response strategy and backup data recovery plan|ControlCost:$700K|ControlEffectiveness:0.25:::Risk8|ControlName:Introduce mandatory financial transaction monitoring and anomaly detection systems|ControlCost:$650K|ControlEffectiveness:0.20:::Risk9|ControlName:Create a vendor risk management framework with contingency planning|ControlCost:$450K|ControlEffectiveness:0.15:::Risk10|ControlName:Establish a fraud detection system with real-time transaction monitoring|ControlCost:$350K|ControlEffectiveness:0.10"}]
[null,{"$f":"IF(LEN(TRIM(Feedback_on_Controls))>0,AI_WITH_FALLBACK(Base_Script&Summary_Input&Full_risk_definition&Control_Query&\"\\nPrevious Generation to Fix: \\n\"&AI_CONTROL_NAME_GEN&\"\\nFeedback from User: \\n\"&Feedback_on_Controls,Temp,4096,model_name),AI_CONTROL_NAME_GEN)","v":"Risk0|ControlDescription:Implement advanced data encryption and access monitoring systems:::Risk1|ControlDescription:Deploy a robust incident response plan with regular ransomware attack simulations:::Risk2|ControlDescription:Establish a comprehensive insider threat detection program with behavioral analytics:::Risk3|ControlDescription:Conduct thorough security audits and compliance checks for all third-party vendors:::Risk4|ControlDescription:Implement multi-factor authentication and regular phishing simulation training for executives:::Risk5|ControlDescription:Enhance encryption protocols and conduct regular penetration testing:::Risk6|ControlDescription:Develop a cryptocurrency payment response strategy and backup data recovery plan:::Risk7|ControlDescription:Introduce mandatory financial transaction monitoring and anomaly detection systems:::Risk8|ControlDescription:Create a vendor risk management framework with contingency planning:::Risk9|ControlDescription:Establish a fraud detection system with real-time transaction monitoring"},null,null,{"$f":"IF(LEN(TRIM(Feedback_on_Costs))>0,AI_WITH_FALLBACK(Base_Script&Summary_Input&Full_risk_definition&\"\\nGenerated list of controls: \\n\"&_xlfn.TEXTJOIN(\":::\",TRUE,'Risk Estimates'!A12:A32&\"|\"&'Risk Estimates'!G12:G32)&\"\\n\"&ControlQuery_2&\"\\nPrevious Generation to Fix: \\n\"&AI_CONTROL_COST_GEN&\"\\nFeedback from User: \\n\"&Feedback_on_Costs,Temp,4096,model_name),AI_CONTROL_COST_GEN)","v":"Risk1|Control Name:Implement multi-factor authentication for sensitive data access|Control Cost:$500K:::Risk2|Control Name:Deploy a robust incident response plan with regular ransomware attack simulations|Control Cost:$750K:::Risk3|Control Name:Establish a comprehensive insider threat detection program with behavioral analytics|Control Cost:$600K:::Risk4|Control Name:Conduct thorough security audits and compliance checks for all third-party vendors|Control Cost:$400K:::Risk5|Control Name:Implement multi-factor authentication and regular phishing simulation training for executives|Control Cost:$300K:::Risk6|Control Name:Enhance encryption protocols and conduct regular penetration testing|Control Cost:$550K:::Risk7|Control Name:Develop a cryptocurrency payment response strategy and backup data recovery plan|Control Cost:$700K:::Risk8|Control Name:Introduce mandatory financial transaction monitoring and anomaly detection systems|Control Cost:$650K:::Risk9|Control Name:Create a vendor risk management framework with contingency planning|Control Cost:$450K:::Risk10|Control Name:Establish a fraud detection system with real-time transaction monitoring|Control Cost:$350K"},{"$f":"IF(LEN(TRIM(Feedback_on_Effectivenesses))>0,AI_WITH_FALLBACK(Base_Script&Summary_Input&Full_risk_definition&\"\\nGenerated list of controls: \\n\"&_xlfn.TEXTJOIN(\":::\",TRUE,'Risk Estimates'!A12:A32&\"|\"&'Risk Estimates'!G12:G32&\"|\")&\"\\n\"&Control_Prompt_3&\"\\nPrevious Generation to Fix: \\n\"&AI_CONTROL_EFF_GEN&\"\\nFeedback from User: \\n\"&Feedback_on_Effectivenesses,Temp,4096,model_name),AI_CONTROL_EFF_GEN)","v":"Risk1|ControlName:Implement multi-factor authentication for sensitive data access|ControlCost:$500K|ControlEffectiveness:0.30:::Risk2|ControlName:Deploy a robust incident response plan with regular ransomware attack simulations|ControlCost:$750K|ControlEffectiveness:0.25:::Risk3|ControlName:Establish a comprehensive insider threat detection program with behavioral analytics|ControlCost:$600K|ControlEffectiveness:0.20:::Risk4|ControlName:Conduct thorough security audits and compliance checks for all third-party vendors|ControlCost:$400K|ControlEffectiveness:0.15:::Risk5|ControlName:Implement multi-factor authentication and regular phishing simulation training for executives|ControlCost:$300K|ControlEffectiveness:0.10:::Risk6|ControlName:Enhance encryption protocols and conduct regular penetration testing|ControlCost:$500K|ControlEffectiveness:0.35:::Risk7|ControlName:Develop a cryptocurrency payment response strategy and backup data recovery plan|ControlCost:$700K|ControlEffectiveness:0.25:::Risk8|ControlName:Introduce mandatory financial transaction monitoring and anomaly detection systems|ControlCost:$650K|ControlEffectiveness:0.20:::Risk9|ControlName:Create a vendor risk management framework with contingency planning|ControlCost:$450K|ControlEffectiveness:0.15:::Risk10|ControlName:Establish a fraud detection system with real-time transaction monitoring|ControlCost:$350K|ControlEffectiveness:0.10"}]
[null,{"$f":"_xlfn.LET(\n    _xlpm.txt, SUBSTITUTE(SUBSTITUTE(TRIM(AI_CONTROL_NAME_CORRECT),CHAR(22),\"\"),CHAR(27),\"\"),\n    _xlpm.parts, _xlfn.TEXTSPLIT(_xlpm.txt,\"|\",\":::\"),\n    _xlpm.nm,    TRIM(INDEX(_xlpm.parts,,1)),\n    _xlpm.cnm,   TRIM(INDEX(_xlpm.parts,,2)),\n    _xlpm.cRaw,  TRIM(INDEX(_xlpm.parts,,3)),\n    _xlpm.eRaw,  TRIM(INDEX(_xlpm.parts,,4)),\n    _xlpm.keep,  _xlpm.nm<>\"\",\n    _xlpm.cnmF,  _xlfn.TEXTAFTER(_xlfn._xlws.FILTER(_xlpm.cnm,_xlpm.keep),\"ControlDescription:\"),\n    _xlpm.nmF,   _xlfn._xlws.FILTER(_xlpm.nm,_xlpm.keep),\n    _xlpm.cRawF, _xlfn._xlws.FILTER(_xlpm.cRaw,_xlpm.keep),\n    _xlpm.eRawF, _xlfn._xlws.FILTER(_xlpm.eRaw,_xlpm.keep),\n    _xlpm.cost, _xlfn.MAP(_xlpm.cRawF, _xlfn.LAMBDA(_xlpm.cs,\n              _xlfn.LET(_xlpm.t, _xlfn.TEXTAFTER(_xlpm.cs,\"$\"),\n                  _xlpm.m, _xlfn.SWITCH(RIGHT(_xlpm.t,1),\n                            \"K\",1000,\"M\",1000000,\"B\",1000000000,\"T\",1000000000000,1),\n                  VALUE(LEFT(_xlpm.t,LEN(_xlpm.t)-1))*_xlpm.m))),\n    _xlpm.eff,  _xlfn.MAP(_xlpm.eRawF, _xlfn.LAMBDA(_xlpm.es, VALUE(_xlfn.TEXTAFTER(_xlpm.es,\":\")))),\n    _xlfn.HSTACK(_xlpm.nmF,_xlpm.cnmF)\n)","v":"Risk0"},"Implement advanced data encryption and access monitoring systems",{"$f":"_xlfn.LET(\n    _xlpm.txt, SUBSTITUTE(SUBSTITUTE(TRIM(AI_COST_CORRECT),CHAR(22),\"\"),CHAR(27),\"\"),\n    _xlpm.parts, _xlfn.TEXTSPLIT(_xlpm.txt,\"|\",\":::\"),\n    _xlpm.nm,    TRIM(INDEX(_xlpm.parts,,1)),\n    _xlpm.cnm,   TRIM(INDEX(_xlpm.parts,,2)),\n    _xlpm.cRaw,  TRIM(INDEX(_xlpm.parts,,3)),\n    _xlpm.eRaw,  TRIM(INDEX(_xlpm.parts,,4)),\n    _xlpm.keep,  _xlpm.nm<>\"\",\n    _xlpm.cnmF,  _xlfn.TEXTAFTER(_xlfn._xlws.FILTER(_xlpm.cnm,_xlpm.keep),\"ControlName:\"),\n    _xlpm.nmF,   _xlfn._xlws.FILTER(_xlpm.nm,_xlpm.keep),\n    _xlpm.cRawF, _xlfn._xlws.FILTER(_xlpm.cRaw,_xlpm.keep),\n    _xlpm.eRawF, _xlfn._xlws.FILTER(_xlpm.eRaw,_xlpm.keep),\n    _xlpm.cost, _xlfn.MAP(_xlpm.cRawF, _xlfn.LAMBDA(_xlpm.cs,\n              _xlfn.LET(_xlpm.t, _xlfn.TEXTAFTER(_xlpm.cs,\"$\"),\n                  _xlpm.m, _xlfn.SWITCH(RIGHT(_xlpm.t,1),\n                            \"K\",1000,\"M\",1000000,\"B\",1000000000,\"T\",1000000000000,1),\n                  VALUE(LEFT(_xlpm.t,LEN(_xlpm.t)-1))*_xlpm.m))),\n    _xlpm.eff,  _xlfn.MAP(_xlpm.eRawF, _xlfn.LAMBDA(_xlpm.es, VALUE(_xlfn.TEXTAFTER(_xlpm.es,\":\")))),\n    _xlfn.HSTACK(_xlpm.cost)\n)","v":500000},{"$f":"_xlfn.LET(\n    _xlpm.txt, SUBSTITUTE(SUBSTITUTE(TRIM(AI_EFF_CORRECT),CHAR(22),\"\"),CHAR(27),\"\"),\n    _xlpm.parts, _xlfn.TEXTSPLIT(_xlpm.txt,\"|\",\":::\"),\n    _xlpm.nm,    TRIM(INDEX(_xlpm.parts,,1)),\n    _xlpm.cnm,   TRIM(INDEX(_xlpm.parts,,2)),\n    _xlpm.cRaw,  TRIM(INDEX(_xlpm.parts,,3)),\n    _xlpm.eRaw,  TRIM(INDEX(_xlpm.parts,,4)),\n    _xlpm.keep,  _xlpm.nm<>\"\",\n    _xlpm.cnmF,  _xlfn.TEXTAFTER(_xlfn._xlws.FILTER(_xlpm.cnm,_xlpm.keep),\"ControlName:\"),\n    _xlpm.nmF,   _xlfn._xlws.FILTER(_xlpm.nm,_xlpm.keep),\n    _xlpm.cRawF, _xlfn._xlws.FILTER(_xlpm.cRaw,_xlpm.keep),\n    _xlpm.eRawF, _xlfn._xlws.FILTER(_xlpm.eRaw,_xlpm.keep),\n    _xlpm.cost, _xlfn.MAP(_xlpm.cRawF, _xlfn.LAMBDA(_xlpm.cs,\n              _xlfn.LET(_xlpm.t, _xlfn.TEXTAFTER(_xlpm.cs,\"$\"),\n                  _xlpm.m, _xlfn.SWITCH(RIGHT(_xlpm.t,1),\n                            \"K\",1000,\"M\",1000000,\"B\",1000000000,\"T\",1000000000000,1),\n                  VALUE(LEFT(_xlpm.t,LEN(_xlpm.t)-1))*_xlpm.m))),\n    _xlpm.eff,  _xlfn.MAP(_xlpm.eRawF, _xlfn.LAMBDA(_xlpm.es, VALUE(_xlfn.TEXTAFTER(_xlpm.es,\":\")))),\n    _xlfn.HSTACK(_xlpm.eff)\n)","v":0.3}]
[null,"Risk1","Deploy a robust incident response plan with regular ransomware attack simulations",750000,0.25]
[null,"Risk2","Establish a comprehensive insider threat detection program with behavioral analytics",600000,0.2]
[null,"Risk3","Conduct thorough security audits and compliance checks for all third-party vendors",400000,0.15]
[null,"Risk4","Implement multi-factor authentication and regular phishing simulation training for executives",300000,0.1]
[null,"Risk5","Enhance encryption protocols and conduct regular penetration testing",550000,0.35]
[null,"Risk6","Develop a cryptocurrency payment response strategy and backup data recovery plan",700000,0.25]
[null,"Risk7","Introduce mandatory financial transaction monitoring and anomaly detection systems",650000,0.2]
[null,"Risk8","Create a vendor risk management framework with contingency planning",450000,0.15]
[null,"Risk9","Establish a fraud detection system with real-time transaction monitoring",350000,0.1]

{"sheet":"AI Inputs","index":1,"n_rows":22,"n_cols":8,"rows_kept":22,"cut":false,"hidden":false,"merged":[[17,2,17,6],[18,2,18,6],[0,0,0,7],[1,0,3,2],[14,3,15,3],[1,4,3,7],[10,5,10,7],[6,2,6,5],[7,2,7,5]]}
[" AI Enabled Risk Register: One-for-One Substitution Model"]
[null,null,null,"Contact HDR to develop custom \nquantitative methods for your firm.","This risk register demonstrates how Artificial Intelligence, combined with expert judgment, can be translated into fully quantitative risk analysis. The model converts likelihood and impact estimates into probabilistic models suitable for simulation, enabling risks to be aggregated, compared, and evaluated on a consistent basis. In the yellow cells below, enter your project and risk-related information. The input fields are structured to capture assumptions and key variables, which the AI model will then process to fill in a list of risks and corresponding estimates. A hidden sheet includes the intermediate calculations performed by the AI model. It translates the inputs into structured estimates that are output in the Risk Estimates tab. Important: All AI generated content should be reviewed and confirmed with subject-matter experts before relying on this model for decision-making."]
[null,null,null,"www.hubbardresearch.com"]
[null,null,null,"info@hubbardresearch.com"]
[]
[]
[null,null,"Prerequisites and Set-up Instructions"]
[null,null,"To use this tool, you need an OpenAI account and available credits.\n  1. OpenAI API Key: Create an account (https://auth.openai.com/create-account) and get an API key from the API Keys page (https://platform.openai.com/api-keys).\n  2. Microsoft Excel Version: Ensure that your version of Excel or organizational policies allow for VBA macros.\n  3. OpenAI API Credits: Make sure you have added credits to your OpenAI account via the Billing page (https://platform.openai.com/account/billing).\n  4. Enable macros and unblock workbook (requires closing file and unblocking within file properties).\n  5. Enter Your OpenAI API Key into the input field below.\n  6. Clear the flag in cell E16 to prevent AI calls while you refine inputs. Enter \"1\" in E16 to enable AI calls.\n  7. Clear illustrative inputs on 'AI inputs' sheet and illustrative overrides in 'Risk Estimates' sheet before using."]
[]
[]
[null,null,"Category","Potential Considerations","Input","SAMPLE INPUTS"]
[null,null,"Organization Profile","• Mission or core purpose\n• Industry / sector\n• Key activities, products, or services\n• Size metrics (employee count, annual revenue, and/or market-share figure)","Generate cybersecurity specific risks to a health insurance company.","Generate potential risks for a large infrastructure project in the construction sector, with a $500M budget and 1,200 workers. Consider its mission (deliver transportation improvements), key activities (design, procurement, build), and project context.","Generate potential risks for a regional hospital system in the healthcare sector, with 3,500 employees and $1.2B annual revenue. Consider its mission (provide patient care), key services (emergency, surgery, outpatient), and industry context. ","Generate cybersecurity specific risks to an insurance company."]
[null,null,"Risk Scope","\t• One top-level risk category (financial, operational, strategic, compliance, etc.)\n• Enumerated sub-categories specific to that category (e.g., credit risk, liquidity risk for “financial”)",null,"Include risks such as schedule delays, cost overruns, safety incidents, and supply chain disruptions.","Include risks such as staffing shortages, regulatory changes, IT failures, and patient safety incidents.","Decompose of sources of business interruption that have real financial outcomes for the purpose of measuring the return on control and mitigation investments. Special attention to root cause attribution to isolate targets for controls and mitigations."]
[]
[null,null,"# of Risks","Enter a value between 1 and 20 to set how many risks you would like generated.","Type 1 to Send Inputs"]
[null,null,10,null,1]
[]
[null,null,"Open AI Key"]
[]
[]
[null,null,"Model Name"]
[null,null,"gpt-4o"]

{"sheet":"Risk Estimates","index":2,"n_rows":32,"n_cols":27,"rows_kept":32,"cut":false,"hidden":false,"merged":[[7,10,7,17],[7,18,7,25],[6,20,6,21],[10,4,10,5],[6,4,6,5],[10,13,10,14],[8,4,8,5],[9,4,9,5],[7,1,7,8],[9,10,9,17],[5,0,5,1],[6,13,6,14],[0,26,0,27],[1,3,3,7],[0,20,0,24],[1,1,3,1],[0,0,0,2]]}
["Risk Register",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"← Uncollapse for HDR PRNG",null,null,null,null,null,"← Uncollapse for Residual Loss and ROC"]
[null,null,"Contact HDR to develop custom quantitative methods for your firm.","This sheet presents the AI-generated risks, the estimates of likelihood and impact for each risk, potential controls, and control effectiveness. These serve as the default values used in the model. You can provide feedback on a given column, and those and all downstream values will be updated. You can manually override the AI estimates in the override fields, ideally based on calibrated expert judgment. The workbook will use your entries instead of the AI defaults. You can also \"lock in\" the estimates or other content the AI provided before giving feedback to change other aspects of the register. This sheet also calculates simulated losses based on probabilistic modeling and the effect of selected controls. This structure allows users to benefit from AI-generated starting points while retaining full control to refine the estimates and ensure they reflect organizational knowledge.",null,null,null,null,{"$f":"GENERATIVEAI(\"TEST\")","v":"Missing API key."}]
[null,null,"www.hubbardresearch.com"]
[null,null,"info@hubbardresearch.com"]
[null,"Trial Slider Bar →",null,"Trial ID:",42]
["↓ Expand for column category details"]
[null,"The \"Risk Name\" should be a short, unique identifier for the risk being considered.","Enter the \"Risk Classification\" that best matches. If the category needs explanation, please use the \"Notes\" column.","Estimate the likelihood of the event occurring at least once per year.","Estimate the total impact of the event, assuming it occurs, with a 90% confidence interval. That is, estimate a lower bound and upper bound, such that you believe there is a 90% chance the actual impact will be between the bounds. Note that the lower bound must be greater than 0.",null,"The \"Control Name\" should be a short, unique identifier for a control that could mitigate the risk in the row.","For the control being applied to the risk, provide an estimate of the cost of implementing the control. ","Estimate the effectiveness of the controls being applied as a percent reduction in the likelihood of the event occurring. For example, if the risk has a 10% chance per year of occurring and a control is applied with 25% effectiveness, the mitigated likelihood of the event occurring would be 7.5%.",null,"The \"Risk Name\" should be a short, unique identifier for the risk being considered.","Enter the \"Risk Classification\" that best matches. If the category needs explanation  please use the \"Notes\" column.","Estimate the likelihood of the event occurring at least one time in a year.","Estimate the total impact of the event, given that it occurred, with a 90% confidence interval. That is, estimate a lower bound and upper bound such that you believe there is a 90% chance the actual impact will be between the bounds. Note that the lower bound must be greater than 0.",null,"The \"Control Name\" should be a short, unique identifier for a control that could mitigate the risk in the row.","For the control being applied to the risk, provide an estimate of the cost of implementing the control. ","Estimate the effectiveness of the controls being applied as a percent reduction in the likelihood of the event occurring. For example, if the risk has a 10% chance per year of occurring and a control is applied with 25% effectiveness, the mitigated likelihood of the event occurring would be 7.5%.","Record any relevant notes about the risk, such as assumptions made when estimating likelihood and impact, or the controls, such as what controls are included in the total cost.","The expected value, or probability-weighted average, of the loss due to a risk. A lognormal distribution is assumed for impact.","Pseudo-random numbers between 0 and 1 are generated for each variable using a unique variable ID. These numbers are then used in other formulas to generate values for each scenario.",null,"A random draw against the likelihood of the event determines if the event occurs. If it does occur, total losses are randomly drawn from the impact ranges. If the event does not occur, there is $0 simulated loss.","The expected value, or probability-weighted average, of the loss after applying controls. A lognormal distribution is assumed for impact.","The loss simulated in this trial, if there is one, after the implementation of the control.","Return on control is computed by comparing the monetized risk reduction (change in expected loss) to the total cost of controls."]
[null,"AI GENERATED INPUT VALUES",null,null,null,null,null,null,null,"User Overrides →","USER OVERRIDE FOR AI GENERATED INPUT VALUES",null,null,null,null,null,null,null,"OUTPUT VALUES"]
[null,"Feedback on Risk Names","Feedback on Categories","Feedback on Probabilities","Feedback on Impact",null,"Feedback on Controls","Feedback on Costs","Feedback on Effectivenesses"]
[null,null,null,null,null,null,null,null,null,null,"Once you are satisfied with a row from the AI generated inputs, or if you would like to edit one or more of the inputs based on expertise, use the area below. These values will override the corresponding rows in the AI generated inputs."]
[null,null,null,null,"90% Confidence Interval of Impact",null,null,null,null,null,null,null,null,"90% Confidence Interval of Impact",null,null,null,null,null,null,100000,100001]
["Risk ID","Risk Name","Risk Classification","Probability of a Loss Over 1 Year","Lower Bound","Upper Bound","Control Name"," Cost of Control","Control Effectiveness",null,"Risk Name","Risk Classification","Probability of a Loss Over 1 Year","Lower Bound","Upper Bound","Control Name"," Cost of Control","Control Effectiveness","Notes","Expected Inherent Loss","Probability Rand","Impact Rand","Simulated Inherent Loss","Expected  Residual Loss","Simulated Residual Loss","Return on Control"]
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{"sheet":"Loss Exceedance Curve","index":3,"n_rows":10009,"n_cols":20,"rows_kept":4500,"cut":true,"hidden":false,"merged":[[0,12,0,13],[4,0,4,2],[0,0,0,11],[5,14,5,15],[5,17,5,19],[6,0,6,4],[6,6,6,9],[6,11,6,12],[6,14,6,15],[6,17,6,19],[18,6,19,7],[18,8,19,9],[26,0,26,2],[1,3,1,5],[1,7,3,17],[2,3,2,5],[3,3,3,5],[7,0,8,4],[7,7,8,7],[7,8,8,8],[15,6,15,9],[16,6,17,7],[16,8,17,9],[5,0,5,4],[5,6,5,9],[5,11,5,12]],"bounds":{"rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0}}
["One-For-One Substitution Model",null,null,null,null,null,null,null,null,null,null,null,"Expand for Residual Loss Data Table →",null,null,null,null,null,"Expand for Residual Losses →"]
[null,null,null,"Contact HDR to develop custom quantitative methods for your firm.",null,null,null,"Introduction: This tab displays the results of the Monte Carlo simulation. Excel's What-If Data Tables are used to run 1,000 random trials based on the Risk Estimates provided, and a histogram shows how often total losses fall within specified bins. Expand the columns using “+” to display the residual risk curve on the Loss Exceedance Curve chart. Ensure that calculation mode is set to \"Partial\" (Formulas → Calculation Options) so that results update correctly."]
[null,null,null,"www.hubbardresearch.com"]
[null,null,null,"info@hubbardresearch.com"]
[null,null,null,"Trial ID:",465]
["The loss exceedance curve displays the probability of exceeding a given loss in one year compared to your organization's willingness to accept that magnitude of a loss (your risk tolerance). Below the chart, the user can enter a loss in the gold cell and the probability of exceeding that loss will be shown.",null,null,null,null,null,"Your organization's risk tolerance can be defined by estimating at least three points on this curve. One way to start with this is to leave the Acceptable Probability Loss Exceeded where it is and adjust the Loss amounts to match your tolerance. Are you willing to accept a 99% chance of losing $1MM or more in a given year? If not, what about $500,000 or more?",null,null,null,null,"This data table calculates the inherent loss that occurs in each trial. A total of 10,000 trials are calculated. Note that these are the losses that occur with no controls present.",null,null,"This data table calculates the residual loss that occurs in each trial. A total of 10,000 trials are calculated. Note that these are the losses that occur after controls are applied.",null,null,"This table calculates the probability of inherent and residual (if controls are included) losses in pre-defined intervals (e.g., the probability of a loss between $500,000 and $999,999). These values are used to plot the loss exceedance curve."]
["Inherent Risk, Residual Risk, and Risk Tolerance",null,null,null,null,null,"Loss Exceedance Tolerance",null,null,null,null,"Total Simulated Inherent Losses",null,null,"Total Simulated Residual Losses",null,null,"Chart Table"]
["Recalculate the workbook to update the curve. Press the F9 key or use \"Calculate Now\" in the Formulas Ribbon.",null,null,null,null,null,null,"Loss","Acceptable Probability Loss Exceeded",null,null,"Trial","Loss",null,"Trial","Loss",null,"Loss","Inherent: % Loss or Greater","Residual: % Loss or Greater"]
[null,null,null,null,null,null,null,null,null,null,null,"Current Trial →",{"$f":"SUBTOTAL(9, 'Risk Estimates'!$W$13:$W$32)","v":0},null,"Current Trial →",{"$f":"SUBTOTAL(9, 'Risk Estimates'!$Y$13:$Y$32)","v":0},null,1E-4,{"$f":" COUNTIF(M$10:M$1009,\">\"&R9)/1000","v":0.76400000000000001},{"$f":"COUNTIF(P$10:P$1009,\">\"&R9)/1000","v":0.66700000000000004}]
[null,null,null,null,null,null,null,100000,0.99,null,null,1,14376779.276746999,null,1,6394822.9473760985,null,{"$f":"R9+500000","v":500000.0001},0.755,0.65400000000000003]
[null,null,null,null,null,null,null,2000000,0.15,null,null,2,7553817.7841992108,null,2,7553817.7841992108,null,{"$f":"R10+500000","v":1000000.0001000001},0.71899999999999997,0.61]
[null,null,null,null,null,null,null,10000000,0.02,null,null,3,0,null,3,0,null,{"$f":"R11*1.07","v":1070000.0001070001},0.71499999999999997,0.60499999999999998]
[null,null,null,null,null,null,null,50000000,5.0000000000000001E-3,null,null,4,14856843.696840581,null,4,14856843.696840581,null,{"$f":"R12*1.07","v":1144900.0001144903},0.70699999999999996,0.59299999999999997]
[null,null,null,null,null,null,null,null,null,null,null,5,24361018.204306759,null,5,24361018.204306759,null,1225043.0001225048,0.70399999999999996,0.58599999999999997]
[null,null,null,null,null,null,null,null,null,null,null,6,2004510.4601457987,null,6,2004510.4601457987,null,1310796.0101310802,0.69899999999999995,0.57699999999999996]
[null,null,null,null,null,null,"Expected Losses",null,null,null,null,7,468447.38262809394,null,7,468447.38262809394,null,1402551.730840256,0.69299999999999995,0.56799999999999995]
[null,null,null,null,null,null,"Expected Inherent Loss",null,{"$f":"AVERAGE(M10:M10009)","v":5660518.2866128897},null,null,8,543198.84355465777,null,8,543198.84355465777,null,1500730.351999074,0.68600000000000005,0.55900000000000005]
[null,null,null,null,null,null,null,null,null,null,null,9,12765186.998865465,null,9,3382986.4265297931,null,1605781.4766390093,0.67500000000000004,0.54800000000000004]
[null,null,null,null,null,null,"Expected Residual Loss",null,{"$f":"AVERAGE(P10:P10009)","v":4012962.3577519753},null,null,10,0,null,10,0,null,1718186.1800037401,0.66,0.53500000000000003]
[null,null,null,null,null,null,null,null,null,null,null,11,21598781.766951218,null,11,1909085.4974232314,null,1838459.2126040021,0.64800000000000002,0.52400000000000002]
[null,null,null,null,null,null,null,null,null,null,null,12,11078737.421939326,null,12,11078737.421939326,null,1967151.3574862822,0.63300000000000001,0.51]
[null,null,null,null,null,null,null,null,null,null,null,13,12795034.122677429,null,13,12795034.122677429,null,2104851.952510322,0.61899999999999999,0.49399999999999999]
[null,null,null,null,null,null,null,null,null,null,null,14,7726171.821902982,null,14,805260.69630752632,null,2252191.5891860449,0.61099999999999999,0.48199999999999998]
[null,null,null,null,null,null,null,null,null,null,null,15,681006.20567758661,null,15,681006.20567758661,null,2409845.0004290682,0.58899999999999997,0.45600000000000002]
[null,null,null,null,null,null,null,null,null,null,null,16,7445869.5716289822,null,16,7445869.5716289822,null,2578534.1504591033,0.57399999999999995,0.441]
[null,null,null,null,null,null,null,null,null,null,null,17,5770626.669487752,null,17,3146980.905586706,null,2759031.5409912406,0.55400000000000005,0.42]
["The Probability of Inherent Loss Exceeding",null,null,2000000,{"$f":"\"is \" &(1-_xlfn.PERCENTRANK.INC(P10:P10009,D27))*100 &\"%\"","v":"is 51.2%"},null,null,null,null,null,null,18,2638647.5942663276,null,18,2638647.5942663276,null,2952163.7488606279,0.54500000000000004,0.41]
[null,null,null,null,null,null,null,null,null,null,null,19,8328844.4778620154,null,19,0,null,3158815.2112808721,0.53300000000000003,0.39400000000000002]
[null,null,null,null,null,null,null,null,null,null,null,20,7104040.2790756598,null,20,3008142.0655779503,null,3379932.2760705333,0.51800000000000002,0.379]
[null,null,null,null,null,null,null,null,null,null,null,21,4187685.1631215597,null,21,4187685.1631215597,null,3616527.5353954709,0.502,0.35699999999999998]
[null,null,null,null,null,null,null,null,null,null,null,22,0,null,22,0,null,3869684.4628731543,0.48399999999999999,0.34300000000000003]
[null,null,null,null,null,null,null,null,null,null,null,23,10821933.527248707,null,23,10821933.527248707,null,4140562.3752742754,0.46,0.31900000000000001]
[null,null,null,null,null,null,null,null,null,null,null,24,16250226.021644749,null,24,16250226.021644749,null,4430401.7415434746,0.439,0.30199999999999999]
[null,null,null,null,null,null,null,null,null,null,null,25,0,null,25,0,null,4740529.8634515181,0.42299999999999999,0.28499999999999998]
[null,null,null,null,null,null,null,null,null,null,null,26,24572362.014888119,null,26,8018921.598847202,null,5072366.9538931251,0.4,0.26800000000000002]
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