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What “allowed” means in practice
There is no single worldwide rule that answers this question. European, UK and US supervisors generally treat public-cloud AI training as a risk-managed outsourcing decision rather than an automatically prohibited activity. Permission is conditional: the bank remains accountable for customer and transaction data, even when a cloud service provider (CSP) stores it, processes it or supplies the machine-learning environment.
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The key distinction is between using cloud infrastructure and giving a provider uncontrolled use of data. A bank may obtain computing capacity while retaining control over the dataset, encryption keys, model artefacts, access rights and deletion. By contrast, uploading identifiable transactions to a consumer-style AI service that can reuse prompts or files for provider training, has unclear data residency, or cannot support regulatory inspection creates a very different risk profile.
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Supervisory expectations apply to more than names and account numbers. FCA guidance (FG16/5, issued in 2018) says “data” includes firm, personal customer and transactional data, as well as system and process data. Logs, feature tables, labels, model outputs and operational metadata can therefore fall within the same governance perimeter.
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The five questions a bank must answer before training in a public cloud
1. Is the processing lawful and necessary?
Document the specific purpose: fraud detection, credit-risk analytics, anti-money-laundering monitoring, customer-service automation or another defined use. Map each field to that purpose, establish the applicable privacy-law basis, set retention periods and remove data that the model does not need. A broad statement such as “improve AI” is not an adequate purpose description.
Legal analysis must cover every relevant jurisdiction. The ECB and EBA materials are EU-focused, FCA guidance is UK-focused and OCC guidance is US-focused. The cited supervisory material does not establish one universal GDPR lawful basis, banking-secrecy exception or AI Act classification. A bank should obtain jurisdiction-specific advice for the countries in which customers, records, staff, CSP operations and support personnel are located.
2. Is the arrangement an appropriately governed outsourcing?
Assess the CSP, the AI platform, implementation partners and every material subcontractor. The assessment should address financial and operational resilience, information security, concentration risk, the provider’s jurisdiction, possible government access, portability and the effect of a provider outage. Classify the service’s materiality and determine whether it supports a critical or important function.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteOutsourcing does not transfer regulatory responsibility. The ECB’s 2025 cloud-outsourcing guide states that supervised entities outsourcing to the cloud retain responsibility for their data. The board and accountable executives therefore need documented ownership, approval thresholds and ongoing oversight rather than a one-time procurement sign-off.
3. Can the bank control location and access?
Specify permitted storage and processing regions and monitor whether the provider complies. Consider replication, backups, support access, telemetry and disaster-recovery sites, not merely the region selected in a console. Evaluate international-transfer mechanisms and the risk that authorities in another jurisdiction could obtain data or encryption keys.
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Contractual location promises should be backed by technical tracing and provider evidence. The ECB recommends restricting storage locations, applying tracing to monitor compliance and ensuring data can be accessed when needed. If a service cannot provide reliable location information, it may be unsuitable for identifiable transaction data.
4. Can regulators and auditors obtain evidence?
Contracts and operating procedures must preserve access for the bank, its auditors and supervisory authorities. FCA guidance requires firms to ensure there are no restrictions on the number of requests the firm, its auditor or the regulator can make to access or receive data. A provider that caps inspections, delays evidence indefinitely or refuses access to relevant subcontractor records creates a governance gap.
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5. Can the bank withstand failure and leave?
Design continuity before production. Define recovery-time and recovery-point objectives for the AI workload, back up training data and configuration, test restoration and failover, and document how the bank would operate if the CSP became unavailable. Exit planning should identify replacement infrastructure, data and model portability, conversion effort, staffing and a realistic timetable.
The EBA’s cloud-outsourcing recommendations, published on 20 December 2017, identify five areas: security of data and systems; location of data and data processing; access and audit rights; chain outsourcing; and contingency plans and exit strategies. These areas remain a useful minimum checklist even when the workload is an AI training pipeline rather than a conventional application.
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Data preparation: reduce exposure before anything reaches the cloud
- Inventory the fields. List direct identifiers, quasi-identifiers, account details, merchant descriptions, geolocation, device data, free text, fraud labels, transaction timestamps, derived features and operational logs.
- Classify sensitivity. Mark personal, confidential, regulated, commercially sensitive and especially sensitive fields. Record the owner, retention rule and permitted users for each class.
- Minimise the training set. Remove fields that do not improve the stated model objective. Shorten historical windows where older records are unnecessary and exclude unrelated customers or products.
- Pseudonymise or tokenise. Replace names, account numbers and other direct identifiers with controlled tokens. Keep the re-identification key in a separately governed bank environment; pseudonymisation is a risk reduction, not automatic anonymisation.
- Protect free text and labels. Merchant narratives, support notes and fraud-investigation comments can contain unexpected personal data. Scan, redact and test samples before export.
- Record lineage. Preserve the source, transformation, consent or other legal rationale, quality checks, feature definitions, training dates and approved dataset version.
Where feasible, train on aggregated or synthetic data for early experimentation and reserve identifiable records for a controlled final stage. Synthetic data still requires validation: unrealistic distributions can hide bias, leakage or poor fraud performance.
Controls expected in the cloud environment
- Encryption: use encryption in transit and at rest, and evaluate encryption in use or confidential-computing options where the threat model warrants them.
- Key control: use customer-managed keys or equivalent separation where appropriate, define who can administer keys, and plan rotation, revocation and recovery.
- Least privilege: separate data engineering, model development, production operations and security administration; require strong authentication and time-limited access.
- Isolation: separate development, test and production accounts or projects; prevent training jobs from writing to uncontrolled storage or external endpoints.
- Monitoring: collect immutable access, export, administrative and data-location logs; alert on unusual downloads, privilege changes and region violations.
- Deletion: define deletion and return of primary data, replicas, snapshots, caches, temporary files and model artefacts at contract termination.
- Incident response: set notification deadlines, investigation duties, evidence preservation and customer or regulator communication responsibilities.
- Personnel controls: vet and train provider staff, restrict support access and record privileged sessions where feasible.
Public-cloud architecture choices
The right design depends on risk, capability and the model’s need for scale. A managed service can shorten deployment time, while a bank-controlled stack can provide stronger control at the cost of more engineering and operational responsibility.
| Decision axis | Managed AI service | Bank-controlled training stack |
|---|---|---|
| Data retention and provider training | Require a contractual no-training/no-retention guarantee, clear deletion terms and technical enforcement; defaults vary by service. | The bank controls software and storage, but must configure and verify every retention path. |
| Geographic and sovereign control | Use approved regions and confirm where support, replication and telemetry operate. | More direct placement control, but the bank must operate all regional restrictions. |
| Encryption and keys | Customer-managed keys and confidential-computing support may be available; verify scope and exclusions. | Maximum design freedom, with full responsibility for key infrastructure and recovery. |
| Isolation | Use private networking, dedicated resources or confidential-computing features where offered. | Isolation is designed and maintained by the bank, including patching and hardening. |
| Audit and regulator evidence | Obtain independent reports, logs and direct-access rights; ensure subcontractor evidence is reachable. | Evidence is closer to the bank, but internal teams must produce complete records. |
| Subcontractor transparency | Contractual notice and objection rights are essential because service chains can be extensive. | Fewer external layers may exist, but software and hardware suppliers still require review. |
| Portability and exit | Test export of data, features, code, model weights and configuration before reliance grows. | Portability may be easier, but specialist staff and replacement capacity must be available. |
| Model governance integration | Connect the service to independent validation, fairness, privacy, approval and monitoring workflows. | Governance can be tailored deeply, with greater implementation burden. |
| Concentration risk | Assess dependence on one hyperscaler, region, identity system or proprietary accelerator. | Assess dependence on internal teams, hardware vendors and a smaller number of alternatives. |
Do not treat a “private” or “dedicated” cloud label as proof of compliance. The bank still has to verify access paths, subcontractors, location, logging, deletion and recovery.
AI-specific governance does not end when the data is secure
Cloud outsourcing controls address the environment; they do not prove that the model is safe or suitable. Establish independent model governance covering data quality, lineage, validation, performance limits, explainability appropriate to the use, bias and fairness testing, privacy leakage, adversarial risk, human accountability and change approval.
Monitor drift after deployment. Transaction patterns, fraud tactics and customer behaviour change, so a model that passed validation can deteriorate. Define thresholds for investigation, rollback and retraining. Keep an inventory of models, datasets, versions, owners, approvals, exceptions and incidents.
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OCC Bulletin 2026-13, issued 17 April 2026, says its guidance covers model development and use, validation and monitoring, governance and controls, and vendor and other third-party products. It also states that generative and agentic AI models are outside that bulletin’s scope. A bank should therefore determine which other supervisory, privacy, consumer-protection and safety requirements apply when the system is generative or agentic rather than assuming the bulletin resolves the issue.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical approval sequence
- Define the use case and criticality. State the decision the model supports, affected customers, tolerance for error and whether the function is critical or important.
- Map data and jurisdictions. Draw the flow from source systems through extraction, feature engineering, training, evaluation, serving, logs, backups and deletion. Mark every country and provider access route.
- Complete privacy and secrecy analysis. Establish purpose, lawful basis, minimisation, retention, data-subject rights, banking-secrecy duties and transfer safeguards for each jurisdiction.
- Assess the provider and chain. Review security, resilience, financial health, concentration, legal-access exposure, subcontractors, support locations and portability.
- Negotiate enforceable terms. Cover permitted processing, confidentiality, no unauthorised model training, regions, keys, deletion, incident notice, audit, regulator access, subcontractor changes and exit assistance.
- Build and test controls. Implement private connectivity, identity separation, encryption, tokenisation, logging, alerts, backup, restoration and region restrictions. Test them with representative data.
- Validate the model independently. Challenge data quality, leakage, bias, privacy, robustness, performance and human-oversight design. Record unresolved limitations and compensating controls.
- Obtain accountable approval. Route the package through information security, privacy, legal, model-risk, operational-resilience and senior-management or board processes required by the bank.
- Operate under continuous review. Reassess after material model, provider, region, subcontractor, law or data changes; monitor incidents, access and performance.
- Exercise exit. Periodically prove that the bank can restore the workload, move data and models, and continue essential controls without the incumbent provider.
Common failure modes and how to correct them
“The data is encrypted, so the project is compliant.”
Encryption does not solve unlawful purpose, excessive collection, uncontrolled keys, provider training, location, subcontractor access or missing regulator rights. Pair cryptography with governance, contracts and evidence.
“A cloud region guarantees the data stays there.”
Backups, support engineers, telemetry and disaster recovery may cross borders. Obtain a complete processing map and monitor it rather than relying on a console setting.
“Pseudonymised means anonymous.”
If the bank or another party can reconnect a token to a person, the dataset remains regulated personal data in many regimes. Protect the key and apply the controls required for the underlying information.
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Certifications and independent reports are useful evidence, not a substitute for a bank-specific materiality assessment, contract, testing and ongoing oversight.
“The pilot is too small to matter.”
A development bucket can expose real customer data, logs and credentials. Apply classification, access, retention and deletion controls from the first experiment, or use properly governed synthetic data.
“We can switch providers later.”
Proprietary feature stores, accelerators, model formats and identity integrations can make exit slow and expensive. Export and restore a representative workload before production dependence develops.
How jurisdiction and use case change the answer
An EU bank must consider the GDPR and applicable financial-supervisory and resilience rules, including requirements affecting outsourcing and international transfers. A UK bank must account for FCA and other UK supervisory expectations, including the access and audit principles in FG16/5. A US bank should map the arrangement to its prudential regulator’s third-party, information-security and model-risk expectations; OCC Bulletin 2026-13 is relevant to the scope it describes, but not a universal authorization for every AI system.
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Customer geography matters as much as the bank’s headquarters. A bank serving customers in several countries may need separate transfer assessments, notices, contractual terms and deletion processes. The AI purpose matters too: a back-office anomaly detector, a credit decision, a sanctions alerting system and a customer-facing generative assistant can have very different privacy, fairness, explainability and consumer-protection consequences.
Bottom line for a decision maker
Public cloud is not automatically off-limits for training on transaction data, and it is not automatically safe. Proceed only when the bank can demonstrate a lawful and necessary purpose, minimised and appropriately protected data, enforceable control over providers and locations, unrestricted evidence access for auditors and supervisors, robust security and monitoring, tested resilience, and a credible exit. Independent model governance must run alongside cloud outsourcing governance. If any of those foundations cannot be evidenced, keep the data in a more controlled environment, redesign the dataset or defer the training project.
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