Service and dependency mapping
Important services are traced to the people, processes, technology, facilities and suppliers they need, to find single points of failure.
GRA · Financial Cyber and Operational Resilience
Spatial financeWhere capital meets hazard, exposure and transition
Reliability and resilience engineering at Financial Cyber and Operational Resilience
Resilience engineering studies how organisations anticipate, absorb, recover from and adapt to disruption. On this platform it frames important services, impact tolerances, dependency mapping, recovery design and learning from incidents.
01Contents
Reliability and resilience engineering at Financial Cyber and Operational Resilience in five parts.
02Application
What reliability and resilience engineering does here.
Reliability and resilience engineering studies how systems fail, how failures can be prevented and how systems absorb, adapt and recover from disruption. It addresses both technical components and the organisations that operate them. In cyber and digital resilience, financial stability and critical infrastructure, resilience engineering identifies critical components and recovery bottlenecks across connected systems. It informs redundancy, spares and restoration priorities. The canonical description is on the STEM Commons.
Recovery strategy comparison
Recovery options are compared for time, data loss and cost under destructive scenarios, informing investment choices by boards.
Learning reviews
Post-incident reviews separate causes from blame and track lessons to changes in design, process and training.
03Spatial finance
Settlement dependencies, and what fails with a node.
How a failure at one financial centre can spread through settlement links. Design links, not real dependencies.
04Data
What the method consumes.
Datasets and records used by reliability and resilience engineering on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Service maps | Dependencies | DECLARED |
| Incident reviews | Failure causes | REPORTED |
| Test results | Recovery performance | OBSERVED |
05Monitor
Reliability model.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Reliability model
Operational resilience · Reliability and resilience · Run R-3432
Single point of failureGRA · Financial Cyber and Operational Resilience
Dependency graph
nodes: assets · edges: dependencies
Failure modes
| N3 | 7992 | 3 | Critical |
| N4 | 8104 | 35 | Normal |
| N5 | 8877 | 43 | Normal |
| N6 | 1638 | 7 | Normal |
Cascade size under removal
06Validation
How results are checked.
Reliability predictions are compared with field failure data. Exercises and post-event reviews check whether recovery performed as planned.
| Check | How it is done |
|---|---|
| Mapping depth | Maps extend beyond first-layer dependencies |
| Scenario severity | Tests include destructive and multi-failure scenarios |
| Closure tracking | Lessons are tracked to verified change |
Common-cause failures and rare events are hard to estimate from limited data. Human and organisational factors resist quantification.
07Exposure model
How a local shock becomes a system outage.
How much of the networks an operator, insurer or lender depends on stays working after a local shock, and how much the coupling between them amplifies it. Teaching model with stated illustrative values, not a prediction. The map on this page shows The Low Countries; the model itself is not tied to a place.
Controls
- Assets per layer
- 18
- Hazard footprint
- 8% of area
- Assets that depend on another layer
- 60%
- Spare capacity (tolerance)
- 30%
Readouts
- Assets hit by the hazard
- 7%
- Assets still working
- 20%
- Amplification of the initial damage
- 10.8 times
- Steps to settle
- 4
Numbers, methods and sources
| Step | Data centres working (%) | Networks working (%) | Power working (%) |
|---|---|---|---|
| 0 | 89 | 94 | 94 |
| 1 | 39 | 44 | 94 |
| 2 | 22 | 17 | 61 |
| 3 | 22 | 17 | 33 |
| 4 | 22 | 17 | 22 |
Methods
- Interdependent networks Buldyrev and others (2010), Catastrophic cascade of failures in interdependent networks, Nature 464
- Overload cascades Motter and Lai (2002), Physical Review E 66
- Betweenness load Brandes (2001), Journal of Mathematical Sociology 25
- Multilayer networks Kivela and others (2014), Journal of Complex Networks 2
- Infrastructure interdependencies Rinaldi, Peerenboom and Kelly (2001), IEEE Control Systems 21(6)
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page's map can draw:
- Major roads Natural Earth
- Railways Natural Earth
- Seaports Natural Earth
- Airports Natural Earth
- Urban extents Natural Earth
- Night lights tonight, outages (VIIRS Day/Night Band) NASA/NOAA VIIRS (Suomi NPP) · register nasa.gibs.context-live-layers
- Natural events (NASA EONET) NASA EONET · register nasa.eonet.events
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Financial Cyber and Operational Resilience in Foundry
- Tested at Financial Cyber and Operational Resilience in Universe
- Codified in Digital, Cyber and AI in Standards
- Published in Financial Cyber and Operational Resilience in Reports
- Taught in Faculty of Technology and Digital Systems in Academy
08Connections
Topics that rely on this method.
09Twin and cascade simulator
Trace a shock to the balance sheet.
Assembled from the Nexus Standards area profile Cyber resilience and digital operational resilience: the shocks it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Pick an initiating shock and step through the cascade to see which exposures it reaches and where losses could concentrate. The Map view places the run on an example geography with open data. Every weight is a stated assumption. An exercise, not a forecast, a valuation, a rating or investment advice. To model a place of your own choosing, open the full System Builder.
Hazards
- Dam failure
- River flood (set off downstream)
- Flash flood (set off downstream)
- Mass displacement (set off downstream)
- Widespread power outage (set off downstream)
- Water supply shortfall (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-12Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)NRS-X6Cyber and digital security riskNRS-H54Hazard family: Signal and platform disruption (NHS-1 cyber.platform)NRS-P10Third-party, outsourcing and concentration riskNRS-H107Hazard family: Digital infrastructure concentration and monoculture (NHS-1 tech.digital-concentration)NRS-K31Domain pack profile: Cyber national resilience (DOMAIN-031)NRS-S7System profile: NXS-EOP (Earth observation and digital twins)
Observation
- Known Exploited Vulnerabilities catalogue (listed for reference)
- National Vulnerability Database (CVE enrichment API) (listed for reference)
- OSV open source vulnerability database (listed for reference)
- Proportion of population covered by a mobile network, by technology
Example geography
- North America: 243 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Surface soil moisture (SMAP), NASA GIBS
10Stewardship
Who maintains the method.
Critical Infrastructure stewards reliability and resilience engineering for the Consortium and reviews how it is applied across platforms.
11Exposure, loss and capital
What it means for exposure, loss and capital.
The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.
Transmission channels
How a shock in Reliability and resilience engineering at Financial Cyber and Operational Resilience reaches credit, market, underwriting, liquidity and operational risk, and feeds back.
NGFS climate scenarios · Network for Greening the Financial SystemIFRS S2 Climate-related Disclosures · ISSBTCFD recommendations · Financial Stability Board TCFDPrinciples for the effective management of climate-related financial risks · Basel Committee on Banking Supervision
12Connected
Where this page leads across the Consortium.
13Questions
Questions and answers.
What are the limits of reliability and resilience engineering in this field?
Common-cause failures and rare events are hard to estimate from limited data. Human and organisational factors resist quantification.
How are results checked?
Reliability predictions are compared with field failure data. Exercises and post-event reviews check whether recovery performed as planned.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method's use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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Global Risks AllianceFinancial Cyber and Operational ResilienceScenario and Stress-TestStrategic Systems
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