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
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.
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.
Important services are traced to the people, processes, technology, facilities and suppliers they need, to find single points of failure.
Recovery options are compared for time, data loss and cost under destructive scenarios, informing investment choices by boards.
Post-incident reviews separate causes from blame and track lessons to changes in design, process and training.
How a failure at one financial centre can spread through settlement links. Design links, not real dependencies.
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 |
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Operational resilience · Reliability and resilience · Run R-3432
nodes: assets · edges: dependencies
| N3 | 7992 | 3 | Critical |
| N4 | 8104 | 35 | Normal |
| N5 | 8877 | 43 | Normal |
| N6 | 1638 | 7 | Normal |
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.
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
Readouts
| 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 |
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:
Across the six pillars, led by Nexus Consortium
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
Systems and couplings
Controls
NS-0 Constitution and principlesNS-9 Contextual UNIs (deployments, instances, programme instances)NS-10 Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11 Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-12 Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)NRS-X6 Cyber and digital security riskNRS-H54 Hazard family: Signal and platform disruption (NHS-1 cyber.platform)NRS-P10 Third-party, outsourcing and concentration riskNRS-H107 Hazard family: Digital infrastructure concentration and monoculture (NHS-1 tech.digital-concentration)NRS-K31 Domain pack profile: Cyber national resilience (DOMAIN-031)NRS-S7 System profile: NXS-EOP (Earth observation and digital twins)Observation
Example geography
Critical Infrastructure stewards reliability and resilience engineering for the Consortium and reviews how it is applied across platforms.
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.
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.
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