Risk Scoring for assets
Buget ₹1500-12500 INR
Job Description:
We are planning to come up with Risk Scoring for assets based on the controls failures (Controls could be anything like policy compliance , Asset management controls etc), These controls carry a score from 1-100 & they are categorized as Critical(90-100), High(70-89), Medium(40-69) and low(1-39) based on the score.
also each asset has business criticality on the scale of 5 , 5 being the highest and there are some requirements as well for e.g. for an asset even if there just one failed critical control and if the asset criticality (acs) of that asset is 5 then the final score should be in the critical range(850-1000).
The data we get is number of failed controls in each category and the average score in each category. In the attached excel file first sheet is the requirements i.e. if the acs is 5 and there are critical controls then the risk score should be critical etc
second sheet shows sample data where score_sev* is aggregate score of that category and sev*_count is count of controls failed in that category for e.g. score_sev5 is aggregate score of severity 5 and sev5_count is count of failed controls in severity 5 category.
the Risk column is the required risk score for e.g. 4 means the score should be in critical range i.e. between 850-1000, Sheet 3 has feature priority we are planning to use.
we are considering number of counts also in feature priority for two reason.
1. if there are two assets one with 1 critical control and acs as 5 and another with 10 critical controls with acs 5 then as per requirement both will have score in critical range but we want the second asset to have score higher than the first.
2. When we want to show risk reduction, when we fix issues which are currently failing then we should be able to see the reduction in score because now the count of failed controls have reduced.
looking for an equation or some approach which will calculate score based on all these features . This is little time bound so immediate help would be appreciated.
16 freelanceri licitează în medie 7231₹ pentru acest proiect
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