Finance
Think global, act local: how data residency regulations influence banks' LLM use
How data residency and transfer rules shape banks' use of LLMs, where AI systems touch regulated data, and architectures that keep that data in-country.
Maria-Elena TzanevAI strategy6 min readUpdated 
In short
Data residency rules decide where banks may store and process customer data, and they apply to LLM prompts, retrieved documents, embeddings, logs and fine-tuning data as much as to core systems. Few laws ban cloud AI outright, but transfer rules, localization rules and outsourcing requirements add up. Banks comply by running models and agent platforms in-region, often self-hosted.
Data residency regulations influence banks' LLM use by limiting where customer data may be stored and processed, and those limits apply to everything an AI system touches: prompts, retrieved documents, embeddings, logs and fine-tuning data. Few laws ban cloud AI outright. Instead, cross-border transfer rules, sector-specific localization requirements and supervisors' outsourcing expectations combine, so banks increasingly run models and agent platforms inside the jurisdiction, often in their own infrastructure.
This guide explains the terms, where LLMs touch regulated data, the types of rules involved, and architectures that let banks use LLMs while keeping data where it must stay. It is general information, not legal advice.
What is data residency?
Three related terms are often confused:
| Term | Meaning |
|---|---|
| Data residency | Where data is physically stored and processed, often chosen by the organization or promised by a vendor |
| Data localization | A legal requirement that certain data stay in a country, or that a copy be kept there |
| Data sovereignty | The principle that data is subject to the laws of the country where it is located, including access by that country's authorities |
For banks, the practical question combines all three: which data may leave which jurisdiction, under what conditions, and who could access it once it does. See our glossary entry on data residency for a short definition.
Where do LLMs touch regulated data?
An LLM application creates more copies of data than most teams expect. Each of these needs a location, an access policy and a retention period:
- Prompts and responses sent to a model, including the customer details an agent adds from other systems.
- Retrieved documents pulled into the model's context from knowledge bases.
- Embeddings in vector stores, which are derived from the source text and can reveal it.
- Traces and logs that record every step of an agent run for debugging and audit.
- Memory that stores conversation history or long-term facts about a customer.
- Fine-tuning data and the adapted model weights trained on it.
- Backups and support access, including where a vendor's staff can see data from.
A model API in another country can receive customer data even when the bank's databases never move, simply because an agent put that data into a prompt.
Which rules affect where banks can process data with LLMs?
Four kinds of rules apply, and a bank usually faces several at once:
| Type of rule | Examples | What it means for LLMs |
|---|---|---|
| Cross-border transfer rules in privacy law | GDPR in the EU, UK GDPR, Switzerland's FADP, Brazil's LGPD, Japan's APPI, South Korea's PIPA | Personal data can leave only with a legal transfer mechanism, such as an adequacy decision or approved contract clauses |
| Sector and national localization rules | India's requirement that payment system data be stored only in India; Russia's rule that citizens' personal data be stored in Russian databases; China's security assessment and other conditions for exporting personal information | Some data must stay in the country, whatever the transfer mechanism |
| Supervisory outsourcing and resilience rules | The EU's DORA and the EBA outsourcing guidelines, MAS guidelines in Singapore, OSFI's third-party risk guideline in Canada, APRA's CPS 230 in Australia, US interagency guidance on third-party relationships | AI vendors and model APIs are third-party services: they need risk assessment, contracts with audit and exit rights, and ongoing oversight |
| Banking secrecy | Swiss, Singaporean and Luxembourg banking secrecy provisions | Client-identifying data may not be disclosed to service providers without safeguards or consent, wherever they are |
Two points are often misunderstood. GDPR does not require data to be stored in the EU; it restricts transfers outside the European Economic Area unless a transfer mechanism applies. And enforcement of localization rules is real: in 2021, India's central bank stopped several international card networks from onboarding new customers until they met its payment data storage rules.
In the EU, the Digital Operational Resilience Act, which applies from 17 January 2025, adds a register of ICT third-party providers and stricter contract, testing and exit requirements. An AI platform or model provider used in a critical function falls within that scope.
How can banks use LLMs and keep data where it must stay?
Most banks combine several of these patterns:
- Deploy in-region. Run the agent platform, vector stores and logs in the jurisdiction whose rules apply, using a local cloud region or the bank's own data center.
- Self-host models for regulated data. Run open-weight models inside your environment for steps that handle customer data, and use hosted models only for data that may leave.
- Route by data classification. Detect personal data in a request, then send it to an in-region model or a person, and send everything else wherever it is cheapest to process.
- Minimize what reaches the model. Retrieve only the fields a task needs and keep identifiers out of prompts where possible.
- Keep traces with the data. Store logs in the same region, with retention periods aligned to your record-keeping obligations.
- Deploy per jurisdiction. International banks often run one deployment per region with shared workflow definitions, so the same agent logic runs everywhere while the data stays local.
Our overview of open-source LLMs covers the model options for self-hosting.
What should banks ask an AI vendor about data residency?
- Where are prompts, responses, embeddings, traces, memory and backups stored, and can we choose the region?
- Which sub-processors and model providers receive our data, and from which countries?
- From where can the vendor's staff access our environment for support?
- Can the whole platform, not only the model, run in our cloud account or data center?
- How is data deleted when we delete a project or end the contract?
- What audit rights, exit support and incident notification terms does the contract include?
- Which certifications does the vendor hold, and is a data processing agreement available?
How does Dynamiq help banks meet data residency requirements?
Dynamiq can be self-hosted in a bank's own AWS, Azure, GCP or IBM Cloud account in the region it chooses, on Red Hat OpenShift, on any Kubernetes cluster or on-prem. In a self-hosted deployment, documents, knowledge bases, traces and credentials live in the bank's own database and storage, and outbound calls go only to the model and tool providers the bank configures. Fully isolated setups are delivered with our engineers, who set up the platform and the models it uses to run entirely inside the bank's boundary.
Guardrails detect PII before text reaches a model, so workflows can route customer data to an in-region model or a person. Dynamiq has SOC 2, HIPAA and GDPR in place, and a data processing agreement is available. Read more about Dynamiq for financial services.
FAQ
What is data residency in banking?
It is the requirement or practice of storing and processing customer and transaction data in specific countries or regions. For banks it covers core systems and every copy an application creates, including LLM prompts, embeddings and logs.
Does GDPR require data to be stored in the EU?
No. GDPR allows personal data to be stored anywhere, but transfers outside the European Economic Area need a legal mechanism, such as an adequacy decision or standard contractual clauses, plus an assessment of the risks in the destination country.
Can banks use cloud-hosted LLMs under data residency rules?
Often, for data that may leave the jurisdiction or through in-region services with the right contracts. For regulated customer data, many banks run models and the agent platform in-region or in their own infrastructure, and route sensitive steps there.
Are embeddings personal data?
They can be. Embeddings derived from personal data can reveal information about the person, so treat vector stores built from customer documents with the same residency, access and retention rules as the source data.
How does DORA affect banks' use of AI vendors?
Under DORA, AI platforms and model APIs are ICT third-party services. Banks must record them in their register of information, assess their risks, secure audit and exit rights in contracts, and monitor them, with stricter requirements when they support critical or important functions.
