Building a Risk Model for Digital Supply Chains in ASEAN
2021
The main objective of this project (phases 1-3) is to explore the possible detrimental effects on the digital economy and wider economy of Open Services infrastructure abuses by malign actors. Is it possible to identify and predict the impact on the economy of a major cyber attack or low-level but continuous detrimental effects of using Open Services as attack vectors?
The phase 1 study uncovered important statistical relationships and dependencies of macroeconomic inputs/outputs and population dynamics on the Open Services, larger digital infrastructure, and their digital health signatures.
The results of phase 1 show that, while having a certain proportion of Open Services is unavoidable even in the advanced economies, every effort should be made to effectively manage such infrastructure to prevent attacks and minimise potential disruption.
The potential disruption and negative impact on the economy could come from a clear negative dependency found in the phase 1 analysis in the labour force dynamics vs. open DNS and SNMP ratios per capita in capital and infrastructure intensive economies.
The phase 2 study shows that it is possible to translate the disruption of the digital infrastructure caused by a cyber attack to the disruption of the Labour Force or Gross Investment. We explain how such a disruption happens, discuss a specific formulaic implementation of this relationship, and show how these dependencies could be integrated as inputs into large-scale macroeconomic causal networks.
Such networks form nodes in larger countries or regional networks incorporating many regions around the world, trade flows, supply chains, and exogenous shocks, including climate, pandemics, and cyber-attacks among many others.
In this third phase of the study, we apply this approach to a simplified model of a single country, by running a simplified simulation of the outputs for an ASRAN member state, Indonesia. This allows us to estimate the disruption impact and its recovery horizon on the Real GDP (and many other macroeconomic parameters), caused by a large-scale cyber attack via open SNMP as a disruption vector. We also estimate the global effect of the disruption spreading to other countries and estimate its magnitude for the case of trade disruption contagion. This enables us to put a specific monetary value on the costs and opportunities of upgrading the digital supply chain of the country and thus provide a valuable tool for economic policy insights.
This will open the path to experiments with such data in the context of large-scale models, and hence to improved scenario planning, and therefore to informed decision-making processes.
Building a Risk Model for Digital Supply Chains in ASEAN
2021
The main objective of this project (phases 1-3) is to explore the possible detrimental effects on the digital economy and wider economy of Open Services infrastructure abuses by malign actors. Is it possible to identify and predict the impact on the economy of a major cyber attack or low-level but continuous detrimental effects of using Open Services as attack vectors?
The phase 1 study uncovered important statistical relationships and dependencies of macroeconomic inputs/outputs and population dynamics on the Open Services, larger digital infrastructure, and their digital health signatures.
The results of phase 1 show that, while having a certain proportion of Open Services is unavoidable even in the advanced economies, every effort should be made to effectively manage such infrastructure to prevent attacks and minimise potential disruption.
The potential disruption and negative impact on the economy could come from a clear negative dependency found in the phase 1 analysis in the labour force dynamics vs. open DNS and SNMP ratios per capita in capital and infrastructure intensive economies.
The phase 2 study shows that it is possible to translate the disruption of the digital infrastructure caused by a cyber attack to the disruption of the Labour Force or Gross Investment. We explain how such a disruption happens, discuss a specific formulaic implementation of this relationship, and show how these dependencies could be integrated as inputs into large-scale macroeconomic causal networks.
Such networks form nodes in larger countries or regional networks incorporating many regions around the world, trade flows, supply chains, and exogenous shocks, including climate, pandemics, and cyber-attacks among many others.
In this third phase of the study, we apply this approach to a simplified model of a single country, by running a simplified simulation of the outputs for an ASRAN member state, Indonesia. This allows us to estimate the disruption impact and its recovery horizon on the Real GDP (and many other macroeconomic parameters), caused by a large-scale cyber attack via open SNMP as a disruption vector. We also estimate the global effect of the disruption spreading to other countries and estimate its magnitude for the case of trade disruption contagion. This enables us to put a specific monetary value on the costs and opportunities of upgrading the digital supply chain of the country and thus provide a valuable tool for economic policy insights.
This will open the path to experiments with such data in the context of large-scale models, and hence to improved scenario planning, and therefore to informed decision-making processes.
Building a Risk Model for Digital Supply Chains in ASEAN
2021
The main objective of this project (phases 1-3) is to explore the possible detrimental effects on the digital economy and wider economy of Open Services infrastructure abuses by malign actors. Is it possible to identify and predict the impact on the economy of a major cyber attack or low-level but continuous detrimental effects of using Open Services as attack vectors?
The phase 1 study uncovered important statistical relationships and dependencies of macroeconomic inputs/outputs and population dynamics on the Open Services, larger digital infrastructure, and their digital health signatures.
The results of phase 1 show that, while having a certain proportion of Open Services is unavoidable even in the advanced economies, every effort should be made to effectively manage such infrastructure to prevent attacks and minimise potential disruption.
The potential disruption and negative impact on the economy could come from a clear negative dependency found in the phase 1 analysis in the labour force dynamics vs. open DNS and SNMP ratios per capita in capital and infrastructure intensive economies.
The phase 2 study shows that it is possible to translate the disruption of the digital infrastructure caused by a cyber attack to the disruption of the Labour Force or Gross Investment. We explain how such a disruption happens, discuss a specific formulaic implementation of this relationship, and show how these dependencies could be integrated as inputs into large-scale macroeconomic causal networks.
Such networks form nodes in larger countries or regional networks incorporating many regions around the world, trade flows, supply chains, and exogenous shocks, including climate, pandemics, and cyber-attacks among many others.
In this third phase of the study, we apply this approach to a simplified model of a single country, by running a simplified simulation of the outputs for an ASRAN member state, Indonesia. This allows us to estimate the disruption impact and its recovery horizon on the Real GDP (and many other macroeconomic parameters), caused by a large-scale cyber attack via open SNMP as a disruption vector. We also estimate the global effect of the disruption spreading to other countries and estimate its magnitude for the case of trade disruption contagion. This enables us to put a specific monetary value on the costs and opportunities of upgrading the digital supply chain of the country and thus provide a valuable tool for economic policy insights.
This will open the path to experiments with such data in the context of large-scale models, and hence to improved scenario planning, and therefore to informed decision-making processes.
