GCRI · Advanced Industry and Robotics
Industrial cyber resilience
Factories and process plants depend on operational technology that was designed for reliability, not security, and is now connected to business networks and suppliers. Ransomware and targeted attacks can stop production, damage equipment or create safety hazards. This page sets out how cyber threats affect industrial operations, how resilience is built and measured, and what evidence serves the operators and regulators involved.
Industrial cyber resilience on the record.
Industrial cyber resilience: how the risk develops.
OT/ICS and connected factories. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Connectivity widens exposure
Remote access, cloud services and integration with suppliers connect control systems to networks attackers can reach.
IT incidents stop production
Ransomware on business systems often halts production because scheduling, logistics and quality systems are unavailable, even if controls are untouched.
Safety and security intersect
Attacks on safety instrumented systems or controls could create hazardous conditions, so security must be part of safety engineering.
Recovery needs manual fallback
Plants that can run or shut down safely without their digital systems recover faster and more safely.
Connected activity in Germany.
Night lights, population density and land cover over Germany, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Connected activity, for this page
About this layer
- Evidence
- Observed
- Source
- VIIRS Black Marble (VNP46A2) (NASA LAADS DAAC)
- Provider
- NASA LAADS DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite radiance, corrected for moonlight and atmosphere
- Newest at build
- 2026-10-12
- Resolution
- about 500 m grid
- Uncertainty
- Satellite night-time radiance for the date shown; lights are a proxy for electricity use and outages, not a measurement of supply or access.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Estimated
- Source
- Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
- Provider
- NASA SEDAC (CIESIN, Columbia University)
- Licence
- CC BY 4.0
- Vintage
- null; Census-based estimate, static (2020)
- Resolution
- about 1 km grid
- Uncertainty
- Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- VIIRS Global Flood Product (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite detection; clouds leave gaps
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite detection; clouds leave gaps. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS NOAA-20 corrected reflectance (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite image
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled (latest year GIBS publishes)
- Source
- MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- yearly; Satellite classification; latest year GIBS publishes
- Newest at build
- 2024-01-01
- Resolution
- about 500 m grid
- Uncertainty
- Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- MODIS Terra EVI (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite index; clouds leave gaps
- Newest at build
- 2026-10-13
- Resolution
- about 250 m grid
- Uncertainty
- Satellite vegetation index for the 8 days to the date shown, a habitat condition indicator; not a measure of species or biodiversity.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
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 Germany.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/NOAA VIIRS · NASA JPL OPERA DIST-ALERTSources 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
- 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/NOAA VIIRS (Suomi NPP). 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).
- Observed (EO): NASA JPL OPERA DIST-ALERT (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
- Enhanced vegetation index, rolling 8 days: MODIS Terra EVI, NASA LANCE · 13 Oct 2026
Industrial control systems connect cyber risk to physical domains.
- WaterCouplingWater treatment shares control technology with industry.What to watchSegmentation
- EnergyCouplingIndustrial loads and generation interact with grids.What to watchRemote access controls
- HealthCouplingA compromised process can release hazardous materials.What to watchSafety instrumented systems independence
Findings are designed for the Global Sector Risk and Resilience series. IEC 62443 sets the security framework; the page publishes no vulnerability detail.
- Anonymised OT incident reviews.
- Segmentation practice studies.
A cascade through interdependent networks.
A hazard footprint takes out co-located assets in every layer; fragmentation, dependency between layers and overload of the survivors then spread the failure. The 3D view separates the layers by depth. Teaching model with stated illustrative values, not a prediction. The map on this page shows Germany; 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
- 15%
- Assets still working
- 26%
- Amplification of the initial damage
- 5.0 times
- Steps to settle
- 4
Numbers, methods and sources
| Step | Data centres working (%) | Networks working (%) | Power working (%) |
|---|---|---|---|
| 0 | 94 | 94 | 67 |
| 1 | 94 | 56 | 50 |
| 2 | 83 | 22 | 22 |
| 3 | 44 | 22 | 22 |
| 4 | 44 | 17 | 17 |
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 Advanced Industry and Robotics in Foundry
- Tested at Advanced Industry and Robotics in Universe
- Codified in Industry, Trade and Supply Chains in Standards
- Published in Advanced Industry and Robotics in Reports
- Taught in Faculty of Technology and Digital Systems in Academy
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Industrial cyber incidents reported | count by sector | annual | Agency incident reporting | Published statistics |
| Production days lost to cyber incidents | days | per incident | Incident records | OBSERVED |
| ICS vulnerability advisories | count | monthly | National advisories | Published statistics |
| Sites with IT and OT segmentation | percent of sites | per assessment | Operator assessments | DECLARED |
| Sites with tested manual fallback | percent of sites | per assessment | Exercise records | REPORTED |
| Remote access connections to OT | count | per assessment | Network inventories | OBSERVED |
Model industrial cyber resilience 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
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| CISA industrial control systems advisories | Vulnerabilities and mitigations in control products | Public domain, US government |
| ENISA reports on industrial and OT security | Threat and good practice analysis | Public, ENISA terms |
| IEC 62443 series | Industrial automation security requirements | Licensed standards, purchase required |
| Joint agency advisories on threats to operational technology | Actor behaviour and mitigations | Public, terms per agency |
| Published incident reviews from manufacturers | Effects and recovery after incidents | Public records, terms per publisher |
How industrial cyber resilience 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 industrial cyber resilience: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
Who decides, and what the evidence serves.
Advanced Industry and Robotics prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Plant operators | How to segment, monitor and keep manual fallback | Security reviews; dependency maps |
| Industrial and safety regulators | What OT security to require | Incident and advisory evidence |
| Suppliers and integrators | How to secure remote access and products | Remote access evidence |
Outputs this topic produces
- OT security reviews against IEC 62443
- Production continuity and recovery exercise designs
- Remote access and dependency maps
- Evidence notes for industrial regulators
What experts ask.
- 01
How do attacks reach control systems?
Pathways through business networks, remote access, suppliers and removable media.
- 02
What are the consequences?
Production loss, equipment damage, safety system interference and environmental release.
- 03
Why is OT different from IT?
Long equipment lifetimes, real-time constraints and safety priorities that limit patching and monitoring.
- 04
How do plants recover?
Manual operation, segmentation, backups and restoration of trusted configurations.
What drives the risk.
Industrial control systems were designed for reliability, not security. Many run legacy software that cannot easily be updated, and some protocols lack authentication. Connectivity for efficiency and remote support has widened the attack surface.
Ransomware on business systems often forces plants to stop production even when control systems are untouched, because scheduling, logistics or safety documentation depend on IT. This coupling is a frequent cause of industrial disruption.
Targeted attacks on control and safety systems are rarer but serious, because they can cause physical damage. Defence in depth, network segmentation and independent safety systems limit consequences.
Supply chains extend risk. Compromised vendor software, integrators and shared platforms can affect many sites at once.
Resilience depends on people as much as technology. Plants that can run key processes manually, keep trusted offline backups and rehearse recovery with operators and engineers together restore operations faster than those relying on technical controls alone.
How evidence is gathered, and where it stops.
| Method or data | What it shows | Its limit |
|---|---|---|
| Network architecture review | Segmentation and access paths | Documentation often lags reality |
| Consequence-driven analysis | Worst credible physical outcomes | Requires joint safety and security expertise |
| Incident reports | Patterns in industrial cyber events | Under-reporting and confidentiality |
| Exercises | Recovery capability | Exercises simplify real attacks |
How it is studied, and what the methods cannot say.
- 01Systems engineeringIntegrated safety and security requirements · Factory and site acceptance testingApplied here
- 02Sensors, IoT and observation networksCondition monitoring · In-line quality inspection · Securing connected sensorsApplied here
- 03Process and industrial safety engineeringHazard and operability studies · Layer of protection analysis · Management of changeApplied here
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
Questions and answers.
Why do cyber attacks stop factories?
What is IEC 62443?
What is manual fallback?
Does the platform publish vulnerabilities?
Why not simply patch control systems?
Does the platform assess a company’s security?
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