Recovery architecture
Recovery plans define the order in which identity, infrastructure and applications are rebuilt, with clean-room environments and verified backups.
GCRI · Cybersecurity and Digital Resilience
Resilience engineering for digital services
Reliability and resilience engineering keeps digital services running through failures and attacks. In this field it designs redundancy, graceful degradation, backups and recovery so that a cyber incident becomes an outage of hours, not weeks.
01Register
Resilience engineering for digital services on the record.
Method recordSTEM method
- MethodReliability and resilience engineering
- StewardCritical Infrastructure
- Applied inCybersecurity and Digital Resilience
- Data3 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
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 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.
Degraded operation modes
Critical services define how they continue with reduced digital capability, such as manual processes, when systems are unavailable.
Recovery time testing
Restores of critical systems from backup are timed in exercises and compared with recovery objectives.
03Geography
Connected activity in Europe.
Night lights, population density, land cover and true-colour imagery over Europe, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 4
Connected activity, for this page
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Connected activity, framed on Europe.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA HLS S30Sources and licences
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- NASA Fire Information for Resource Management System (FIRMS), VIIRS 375 m active fire product, NASA LANCE; vector tiles via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Observed (EO): NASA HLS S30 (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- Optional layers (5 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Active fires and thermal anomalies: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
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 |
|---|---|---|
| Backup and restore test records | Recovery performance | OBSERVED |
| Continuity plans | Degraded modes | DECLARED |
| Incident timelines | Real recovery | OBSERVED |
05Simulator
Stocks, flows, delays and a tipping threshold.
Two stocks, pressure and adaptive capacity, joined by a reinforcing growth loop, a delayed balancing response and damage when pressure outruns capacity; past the threshold the system can tip. Teaching model with stated illustrative values, not a prediction. The map on this page shows Europe; the model itself is not tied to a place.
Controls
- Growth of pressure
- 6.0% a year
- Efficacy of capacity in reducing pressure
- 4.0% a year
- Investment response to perceived pressure
- 20%
- Perception delay
- 4.0 years
- Tipping threshold of pressure
- 60 index
- Amplification past the threshold
- 2.0 times
- Years simulated
- 50 years
Readouts
- Peak pressure
- 113 index
- Capacity at the end
- 186 index
- Years over the threshold
- 15.8 years
Numbers, methods and sources
| Year | Pressure | Capacity | Perceived |
|---|---|---|---|
| 0 | 20.0 | 50.0 | 20.0 |
| 4 | 23.3 | 44.9 | 21.2 |
| 8 | 27.2 | 42.2 | 24.0 |
| 12 | 31.7 | 42.3 | 27.7 |
| 16 | 36.8 | 45.5 | 32.2 |
| 20 | 42.3 | 51.5 | 37.2 |
| 24 | 47.9 | 60.3 | 42.6 |
| 28 | 53.2 | 71.3 | 48.0 |
| 32 | 57.8 | 84.3 | 53.1 |
| 36 | 66.4 | 98.4 | 58.4 |
| 40 | 82.7 | 115.9 | 69.7 |
| 44 | 97.8 | 139.7 | 83.8 |
| 48 | 109.1 | 169.4 | 97.3 |
Methods
- System dynamics Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics
- Critical transitions and tipping points Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461
- Causal loop diagram Sterman (2000), Business Dynamics, McGraw-Hill
- 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
No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme's own evidence.
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Cybersecurity and Digital Resilience in Foundry
- Tested at Cybersecurity and Digital Resilience in Universe
- Codified in Digital, Cyber and AI in Standards
- Published in Cybersecurity and Digital Resilience in Reports
- Taught in Faculty of Technology and Digital Systems in Academy
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 |
|---|---|
| Restore testing | Backups are restored in tests, not assumed recoverable |
| Isolation verification | Backup systems are checked for separation from production credentials |
| Exercise realism | Exercises include scenarios where primary tools are unavailable |
Common-cause failures and rare events are hard to estimate from limited data. Human and organisational factors resist quantification.
07Twin and cascade simulator
Model resilience engineering for digital services as a system. Run the cascade.
Assembled from the Nexus Standards area profile Cyber and digital resilience law alignment: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Dam failure
- Sea-ice loss
- Coastal erosion (set off downstream)
- River flood (set off downstream)
- Flash flood (set off downstream)
- Mass displacement (set off downstream)
- Biodiversity loss (set off downstream)
- Widespread power outage (set off downstream)
- Water supply shortfall (set off downstream)
- Arctic winter sea-ice collapse (set off downstream)
- Polar vortex disruption (set off downstream)
Systems and couplings
Controls
NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-9Contextual UNIs (deployments, instances, programme instances)NRS-X6Cyber and digital security riskNRS-H93Hazard family: Identity, trust and cryptographic failure (NHS-1 cyber.identity)NRS-K31Domain pack profile: Cyber national resilience (DOMAIN-031)NRS-H54Hazard family: Signal and platform disruption (NHS-1 cyber.platform)NRS-H86Hazard family: Telecom and data-centre failure (NHS-1 infra.digital)NRS-H94Hazard family: Systemic cyber events (NHS-1 cyber.systemic-cyber)
Observation
- NIST National Vulnerability Database APIs (listed for reference)
- CISA Known Exploited Vulnerabilities catalogue (listed for reference)
- EUR-Lex Cellar (EU legal acts and delegated acts) (listed for reference)
- Cybersecurity Supply Chain Risk Management
- Legal measures pillar of the Global Cybersecurity Index
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
- Vegetation disturbance alerts, 30 m (OPERA DIST-ALERT), NASA GIBS
08Connections
Topics that rely on this method.
09Methods and evidence
How resilience engineering for digital services is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
The couplings between the systems in resilience engineering for digital services: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
10Stewardship
Who maintains the method.
Critical Infrastructure stewards reliability and resilience engineering for the Consortium and reviews how it is applied across platforms.
11Connected
Where this page leads across the Consortium.
12Questions
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.
ReportsContribute
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GCRICybersecurity and Digital ResilienceStrategic SystemsScenario and Stress-Test
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