Data residency refers to the concept of where an organization's data is physically or geographically stored. Data protection regulations may require organizations to keep certain data within the borders of the country or region where it was collected. As cloud computing and artificial intelligence workloads become more widespread, knowing where data resides is no longer just a technical detail; it has become a corporate responsibility that carries direct legal and financial risks.

Following the implementation of the European Union's General Data Protection Regulation (GDPR), countries such as Australia, Brazil, Canada, Japan, South Africa, and the United Arab Emirates have enacted their own data protection laws. The cost of this trend is not abstract; in 2023, Meta was hit with a record-breaking fine for failing to comply with the GDPR. In May 2023, Meta, the parent company of Facebook, was ordered to pay $1.3 billion (€1.2 billion) to the European Union for GDPR non-compliance. This penalty is a clear signal that regulators no longer overlook data residency violations and serves as a wake-up call for every organization.

What Is Data Residency?

There are three distinct but often confused concepts under the umbrella of data residency, and distinguishing between them is the first step toward building an accurate compliance strategy.

Data residency is the physical or geographic location of an organization's data. Under data privacy laws like the GDPR, organizations may be required to store specific data within the country or region where it was collected. Data localization goes a step further, referring to an obligation that mandates data remain within a specific location and jurisdiction; in other words, while residency is a result, localization is a legal requirement that enforces that result. Data sovereignty, on the other hand, concerns the rights and control based on the jurisdiction where data is stored and processed; it determines who can access the data under what conditions and which country's laws apply to that data.

The difference between these three concepts has significant practical consequences. An organization might keep its data in a specific region (residency), but this may be a contractual choice rather than a legal requirement. Conversely, in some sectors (public, finance, healthcare), data localization is imposed as a direct legal or contractual obligation, leaving the organization with no choice in the matter.

What Makes Data Residency Complex in the Cloud?

How cloud resources are positioned and utilized is the primary factor that complicates data residency. There are three main types of cloud provisioning: advanced, dynamic, and user-allocated. All of these carry some degree of risk for data, but the greatest threat comes from dynamic provisioning, where resources are allocated on demand.

The nature of cloud-native workloads also increases this complexity. Ephemeral microservices that appear and disappear in the cloud can lead to data access and movement that is difficult to detect and monitor. Cloud-native applications consist of small, interdependent services such as APIs, endpoints, service meshes, containers, and container orchestrators. These components transfer or move data between one another and may harbor security vulnerabilities that could lead to undetected data loss or theft.

This structural complexity makes it increasingly difficult for organizations to provide a simple answer to the question, "Where is our data?" Even if an organization's primary database is hosted in Turkey, the analytics service, backup system, or third-party integration connected to that data might be running in a different geographic region, requiring the "processing" and "transfer" layers to be evaluated separately.

How Do AI Workloads Change Data Residency Risk?

AI workloads add a new layer to the data residency problem. When an organization sends a query to an LLM API, that data typically travels to the region where the model provider's data center is located; this region may not be in the same country as the organization's own primary data center.

At this point, it is necessary to distinguish between two different data flows. Training data is data used in the development of a model and usually remains on the provider's infrastructure for a long time. Inference data, however, consists of the queries and context information that an organization sends to the model in daily use; this data is usually deleted after processing, but at the moment it is processed, it may be subject to a different jurisdiction, even if only temporarily. Organizations often confuse the two and misinterpret the assurance that "our data is not used to train the model" as meaning that the data never leaves the country.

This distinction also explains why the Sovereign AI approach we discussed earlier is gaining importance. Sovereign AIaims to build an architecture where the model itself and the processing infrastructure remain within a specific jurisdiction, ensuring that even inference data does not cross borders. While not necessary for every organization, this approach offers a stronger guarantee than contractual commitments for regulated sectors or organizations working with sensitive data.

What Do Data Residency and the KVKK Require in Turkey?

At the center of data residency discussions in Turkey is the Personal Data Protection Law (KVKK) No. 6698. With the amendment made to Article 9 of the Law in 2024, the regime regarding the transfer of personal data abroad has become more flexible; data transfer is now possible through mechanisms similar to the GDPR, such as adequacy decisions, binding corporate rules, and explicit consent. This change has significantly softened the previous default restriction that "data cannot be transferred abroad."

However, this flexibility does not cover all sectoral regulations. Beyond the KVKK, restrictive provisions regarding the hosting of data within Turkey remain in effect in various legislations; specifically in banking, insurance, and certain public data categories, sectoral regulations impose direct localization requirements. Therefore, an organization being compliant with the general KVKK regime does not mean it is exempt from sector-specific localization obligations.

On a practical level, this requires two separate checks for organizations working with third-party cloud or AI providers. First, verifying whether the provider meets the requirement for a commitment to adequate protection under Article 9 of the KVKK in the data processing agreement. Second, ensuring that the organization's own obligations—such as obtaining explicit consent, specifying the purpose of processing, and maintaining a data inventory—are fully met. The contractual commitment on the provider's side does not eliminate the organization's own obligations; this is the reflection of the shared responsibility model in cloud security within the context of data residency.

How Do Organizations Manage Data Residency?

Ensuring data residency, localization, and sovereignty requires two fundamental capabilities. The first is technology that detects the location of data in the cloud, as well as its copies and movement. The second is technology that centralizes, analyzes, and reports on the compliance posture of cloud environments.

Data Security Posture Management (DSPM) platforms provide these capabilities by increasing visibility into user activity and behavioral risk. A DSPM solution discovers and classifies sensitive data and data copies in the cloud, enabling organizations to resolve existing and potential issues regarding data residency, localization, and sovereignty. Such a platform helps organizations understand where regulated data resides within complex cloud environments, uncover and classify shadow data, and learn how data actually flows to mitigate regulatory risks.

This approach aligns directly with the data-layer guardrails we discussed in our previous AI guardrails content; data residency monitoring is essentially an enterprise-scale implementation of data guardrails. While not sufficient on its own, it is a practical tool that operationalizes the data layer of a guardrail architecture.

When Is a Local Data Center Mandatory, and When Is a Contractual Commitment Sufficient?

This decision depends on the organization's industry, the category of data it processes, and the assurances provided by its vendor.

In regulated sectors such as public institutions, banking, insurance, and healthcare, data localization is often a legal or contractual requirement, leaving the organization with little room for choice; in these cases, selecting a local data center is a necessity rather than a preference. Conversely, for organizations processing general commercial data and not operating in a regulated sector, the contractual commitment provided by the vendor (such as the assurance of adequate protection under Article 9 of the PDPL) often forms a sufficient basis for compliance.

A concrete example can clarify this distinction: A global cloud service like Microsoft 365 does not yet have its own data center in Turkey and stores customer data in data centers within the nearest European Union region. This does not constitute a PDPL violation on its own because the provider offers an adequate protection commitment that is GDPR-compliant; however, the customer must still fulfill additional obligations on their end, such as obtaining explicit consent and specifying the purpose of processing. The same logic applies to AI providers: the provider's data processing agreement and region options do not replace the organization's own compliance obligations; they complement them.

Frequently Asked Questions

Are data residency and data localization the same thing?No. Data residency is a state referring to where data is actually stored; it may result from a contractual preference. Data localization, on the other hand, is a legal requirement that mandates data remain in a specific location. An organization may choose to keep data in a specific region even without a legal requirement; this is residency, not localization.

Does the PDPL completely prohibit personal data from leaving Turkey?No, not anymore. With the 2024 amendment, Article 9 of the PDPL transitioned to a more flexible regime that allows for cross-border data transfers through mechanisms such as adequacy decisions, binding corporate rules, and explicit consent. However, some sectoral regulations still maintain their own localization requirements.

Which sectors are subject to mandatory data localization?Data localization is generally a legal or contractual requirement in heavily regulated sectors such as the public sector, banking, insurance, and healthcare. Organizations in these sectors must check their specific sectoral legislation independently of the flexibility provided by the general PDPL regime.

What should be considered regarding data residency when choosing an AI provider?It is necessary to check the provider's data center regions, how they process training data versus inference data separately, and whether their contract meets the adequate protection requirement under Article 9 of the PDPL. For organizations in regulated sectors, sovereign AI architectures—where data never crosses borders—offer a stronger guarantee.

TL;DR

Data residency refers to where data is physically located; data localization refers to that location being legally mandated; and data sovereignty refers to the rights and control associated with that location. The dynamic and distributed nature of the cloud makes it difficult to track the actual location of data. AI workloads add a new dimension to this problem, as training data and inference data may be subject to different jurisdictions. In Turkey, the relaxation of Article 9 of the PDPL in 2024 has facilitated cross-border transfers, but sectoral localization requirements remain in effect. While the necessity for a local data center is strict in regulated sectors, a contractual commitment is often a sufficient foundation for general commercial data.

Conclusion

Data residency is no longer just a legal department issue; it is now a direct component of technology architecture decisions. The cloud provider, AI platform, and data center region an organization chooses determine compliance risk from the outset; fixing these issues later is far more costly than designing them correctly from the start.

Review the data processing agreements of your current cloud and AI vendors: which data center regions are you hosted in, and do these contracts explicitly meet the adequate protection requirements under Article 9 of the PDPL? If you operate in a regulated industry, start by clarifying your sector-specific localization obligations; general PDPL flexibility does not supersede industry-specific mandates.

Resources:

• Personal Data Protection Authority, Guide on the Transfer of Personal Data Abroad — https://www.kvkk.gov.tr/Icerik/8142/Kisisel-Verilerin-Yurt-Disina-Aktarilmasi-Rehberi

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