GRA · Financial Cyber and Operational Resilience
Spatial financeWhere capital meets hazard, exposure and transition
Data integrity and availability
Finance runs on records: account balances, positions, policies and transactions. An attack or error that corrupts or destroys them is harder to recover from than an outage, because the firm must restore a correct state. This page sets out how data integrity and availability are threatened and protected, how recovery is measured, and who decides.
Data integrity and availability in seven parts.
- 01The questionData integrity and availability: how the risk develops
- 02IndicatorsWhat is measured, and how well
- 03SourcesWhere the evidence comes from
- 04DecisionsWho decides, and what the evidence serves
- 05Core questionsWhat data resilience asks
- 06MechanismsWhy integrity and availability matter so much
- 07Methods and limitsHow data resilience is built
Data integrity and availability: how the risk develops.
Confidentiality, integrity and continuity. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Corruption spreads to backups
Replication copies corrupted data to secondary sites. Without immutable, isolated backups, there may be no clean copy to restore.
Recovery points decide loss
The time between the last clean copy and the incident sets how much data must be reconstructed from other sources.
Reconciliation proves correctness
After recovery, balances and transactions must be reconciled with counterparties and infrastructures to confirm the restored state.
Errors propagate to others
Incorrect data sent to payment systems, clearing houses or customers spreads the problem beyond the firm.
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.
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 |
|---|---|---|---|---|
| Critical data with immutable backups | share of critical datasets | periodic | Firm records | DECLARED |
| Recovery point objective for critical data | minutes or hours | on review | Firm standards | DECLARED |
| Restore tests from isolated backups | count and success rate | annual | Test records | DECLARED |
| Time to reconcile after restore | hours | per test or incident | Test records | OBSERVED |
| Data corruption incidents | count | annual | Incident records | REPORTED |
| Participation in data vaulting arrangements | yes or no | on change | Firm and industry records | DECLARED |
Important business services.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Important business services
Data integrity and availability · Bank B services · Self-assessment 3
Services vs impact tolerance
| Make a payment | 3 | 2 | Cloud region loss | Within |
| Access cash | 13 | 18 | Cyber attack | Within |
| Mortgage servicing | 62 | 42 | Data centre loss | Within |
| Claims payment | 25 | 50 | Third-party failure | Outside |
Dependency map
people · process · technology · third parties
Remediation
- R-1Second claims platform supplierExit plan tested138 d
- R-2Immutable backups for paymentsCyber recovery44 d
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 |
|---|---|---|
| Sheltered Harbor standard and participant information | United States industry standard for data vaulting and restoration | Free to read, terms to check |
| CPMI-IOSCO cyber guidance on data integrity | Expectations for recovering correct data | Free, BIS terms |
| NIST special publications on data integrity | Guidance on detecting and recovering from data corruption | Public domain |
| DORA requirements on backup and restoration | EU backup and recovery expectations | Free, EU reuse terms |
| Post-incident reports involving data corruption | Lessons from integrity incidents | Public records, terms per publisher |
Trace a shock in data integrity and availability 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
Who decides, and what the evidence serves.
Financial Cyber and Operational Resilience 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 |
|---|---|---|
| Chief technology and data officers | How to protect and recover critical data | Backup architecture and test results |
| Boards | What recovery points are acceptable for critical services | Impact analysis and cost evidence |
| Supervisors | Whether data recovery capabilities meet expectations | Test evidence and incident reviews |
| Industry bodies | Whether to establish shared vaulting or restoration arrangements | Standards and participation |
Outputs this topic produces
- Guide to integrity recovery and reconciliation
- Brief on immutable and isolated backups
- Method note on recovery point objectives
- Questions for boards on data recovery assurance
What data integrity and availability 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 data integrity and availability reaches credit, market, underwriting, liquidity and operational risk, and feeds back.
What data resilience asks.
- If data were corrupted or encrypted, could the firm restore a trusted version, and how far back would it have to go?
- How would the firm know that data had been subtly altered rather than destroyed?
- Are backups isolated from the systems an attacker could reach?
- How would counterparties and the market be assured that balances and positions are correct after an incident?
Why integrity and availability matter so much.
Confidentiality breaches dominate headlines, but integrity and availability failures can stop the financial system working. A bank that cannot trust its own ledger cannot safely open for business.
Data exposure
Breaches harm customers and enable fraud, and carry legal and reputational consequences.
Trusted records
If records cannot be trusted, transactions cannot be settled with confidence.
Access when needed
Encryption by ransomware or outages can make data unavailable just when it is needed.
How data resilience is built.
- 01
Isolated and immutable backups
Backups that attackers cannot alter or delete. Limits: restoring large systems takes time.
- 02
Integrity verification
Cryptographic checks and reconciliation to detect alteration, grounded in cybersecurity and cryptography. Limits: subtle manipulation can go undetected.
- 03
Data recovery testing
Testing that restoration works within tolerances. Limits: full-scale tests are disruptive.
- 04
Sector data vaulting arrangements
Industry initiatives to preserve critical account data for recovery. Limits: coverage and governance vary.
How it is studied, and what the methods cannot say.
- 01Software and safety-critical systems engineeringChange control for core systems · Integrity by design · Fault toleranceApplied here
- 02Reliability and resilience engineeringService and dependency mapping · Recovery strategy comparison · Learning reviewsApplied here
- 03Cybersecurity and cryptographyIdentity and privileged access · Detection and response capability · Threat-led testingApplied here
Questions and answers.
Why is data corruption harder than an outage?
What is an immutable backup?
What is data vaulting?
Are backups enough?
Why is integrity harder than confidentiality?
Does the platform handle firms' data?
How far back might a firm need to restore?
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