Data Sovereignty in the Age of Generative AI: A New Compliance Frontier

The rise of generative artificial intelligence (AI) has fundamentally changed how organizations interact with data.¹ Every industry has been impacted, from marketing and content creation to software development and cybersecurity. As organizations race to harness the power of generative AI within business processes, data sovereignty is a critical area that is often overlooked. Defined as “the concept that data is subject to the laws of the country or region where it was generated,” data sovereignty can become a blind spot for organizations if it is not considered when adopting generative AI.²

Generative AI systems are different from traditional applications because they are trained on extremely large datasets that are global in origin, and thus hard to audit or govern. Interacting with a generative AI system via prompts and receiving generated responses may involve data traversing different jurisdictions and borders. This raises several data residency-related concerns that organizations may not be prepared for. It is crucial that organizations looking to implement generative AI technologies are aware of the risk involved. Understanding this risk is the only way to effectively utilize generative AI and other emerging technologies.

The Changing Landscape of Data Sovereignty

Data sovereignty challenges have typically revolved around technologies such as cloud computing and cross-border data flows. These challenges involve organizations storing data in jurisdictions where they are not authorized to do so. Regulations such as the EU General Data Protection Regulation (GDPR) were established to reduce this risk by providing individuals and regulators with greater control over who has the authority to store, process, and access their personal data.³

However, the massive adoption of generative AI has introduced a new challenge by adding another layer of complexity to data sovereignty, and traditional compliance regulations may not be fully equipped to handle it.

Generative AI systems are trained on massive, global datasets, and their inference processes, in which responses are generated based on user prompts, are often opaque to an organization.

Due to the opaque nature of these systems, several questions—and thus challenges—arise:

• Where is the generative AI system being hosted?
• Is the generative AI system being trained on theprompts provided?
• Are these prompts stored or removed once aresponse is given?
• How can the organization ensure that the promptsand responses do not inadvertently revealpersonally identifiable information (PII) or othersensitive data?

The source of the training, the model location, and the reliability of prompt responses become concerns in this new compliance landscape. Generative AI is a new frontier for data sovereignty, where worries about data are no longer restricted to infrastructure controls and data stores but now extend to generative AI algorithms, model hosting, and prompts. Organizations must update their compliance frameworks to meet these new challenges and move beyond the traditional understanding of data sovereignty.

Generative AI Data Sovereignty Challenges

Generative AI applications' unique nature makes it difficult for organizations to trace where data is being processed and where the responsibility for protecting it lies.

Origins of Training Data
Generative AI models learn by scraping massive datasets from the internet and other proprietary sources. This data has the potential to include personal and copyrighted information collected without explicit consent.

This raises several concerns, such as:

• Organizations adopting generative AI applications may not be aware that the model’s training datasets include sensitive or regulated data. This makes compliance with data sovereignty principles exceptionally challenging.
• Suppose a generative AI model being used in a specific region, such as the European Union, is trained on datasets from another region. In that case, the original rights to that data may be violated, along with cross-border data regulations.
• Data protection frameworks, such as the GDPR, emphasize data subjects' rights, including their right to know how their personal data is being used. Unfortunately, generative AI models offer little transparency or traceability, making it difficult for organizations to demonstrate compliance with various frameworks.

Model Inference
Apart from the training phase, a generative AI model's inference processes (in which prompts are processed and responses generated) can introduce risk to cross-border data flows that violate data residency principles. These applications are often black boxes with little transparency regarding where data is logged, reused, or processed.

Take, for example, a German enterprise that uses a generative AI model hosted in a US cloud region. Employees of the enterprise might unintentionally provide sensitive personal information in their prompts, which could violate data sovereignty and compliance obligations. Whether or not this data is saved permanently, it is still processed in a jurisdiction with very different legal requirements, leading to a potential breach of data transfer laws.

The Risk of Shadow AI
Generative AI’s ability to offer unprecedented gains in productivity and time savings can be a double-edged sword. Employees eager to use this new technology may bypass or ignore organizational mandates around generative AI use, resulting in the rise of shadow AI. Shadow AI refers to the use of generative AI models and tools without centralized compliance oversight. This can result in employees sending sensitive information to public generative AI tools such as ChatGPT⁴ without understanding data residency and sovereignty implications.

Public generative AI services are often hosted across multiple global regions, increasing the risk of data exposure and cross-border data processing. Even if a generative AI provider claims not to retain this data, there is often no way of independently verifying this claim, which increases the risk of privacy and regulatory breaches.

Lack of Transparency

A common requirement for most jurisdictions is transparency into how and where personal or sensitive data is processed. Generative AI systems, however, are often black boxes that offer little traceability into their internal logic or decision-making processes.

This can make it difficult for organizations to demonstrate compliance with requirements such as the right to explanation, given that generative AI outputs are often based on undocumented or opaque processes.

Regulatory audits may also require traceability regarding how these tools reached a particular decision or whether the data stays within the necessary jurisdiction, which most generative AI systems cannot provide. This information can become essential if a data breach or misuse of AI is detected, exposing the organization to negative publicity and potential fines.

How to Ensure Data Sovereignty in Generative AI Systems

Generative AI challenges traditional notions of data sovereignty and requires organizations to adopt a new approach to meeting compliance requirements. There are some key strategies that can be employed to address these challenges while enabling organizations to leverage the value and productivity offered by generative AI.

Implement Controls Around Generative AI Model and Data Hosting
A critical step in respecting data sovereignty laws in generative AI usage is to identify where generative AI models are hosted and to restrict data processing to those authorized jurisdictions. This can be done in several ways:

• Cloud platforms, such as Amazon Web Services (AWS) and Microsoft Azure, often provide controls that allow users to specify the generative AI model's geographic location and where it will process data. Organizations should mandate the usage of these region-specific endpoints to meet their compliance needs.
• Organizations can also utilize sovereign cloud platforms⁵ for sensitive sectors such as government, healthcare, and finance. Some examples are AWS GovCloud and Azure Government.⁶ Organizations should consider deploying generative AI solutions within these environments to control model hosting and inference.
• Organizations can opt for private, self-hosted models within their infrastructure to retain complete control over every aspect of the generative AI model. While this increases the overhead for infrastructure and data storage, it also allows organizations to meet data sovereignty requirements without sacrificing control.

Federated Learning and Privacy-Preserving AI Techniques
Federated learning allows generative AI model training to occur locally or on regional servers without transferring data to another location. Generative AI only learns from the data within its local environment, which can be an effective solution for organizations that need the generative AI system to be trained on sensitive datasets without compromising data sovereignty. For example, a hospital within the European Union can train a generative AI system on its internal data, while ensuring that patient data never leaves national borders.

Additionally, organizations can utilize privacy-preserving techniques, such as differential privacy, which adds noise to datasets to prevent the identification of individuals, or synthetic data, which allows generative AI to generate anonymized data that mimics real-world datasets without exposing sensitive information.

Flow Mapping for Generative AI Inputs and Outputs
An essential requirement for data sovereignty is understanding what data is being handled within organizational generative AI systems. Organizations can meet this challenge in a variety of ways:

• An enterprise-wide data classification program to identify and differentiate between public, sensitive, and regulated data
• Data flows to document the journey of data, from user prompts, to generative AI model processing, to output generation and storage
• Solutions such as data leakage protection (DLP) and cloud access security brokers (CASB), used to monitor and prevent sensitive data from entering public generative AI systems
• Variation between rules and controls regarding the types of data allowed for input into generative AI systems (e.g., PII and financial records may be restricted for public AI application programming interfaces [APIs] but permitted in secure, private deployments)

Extend Governance to Shadow AI
The hidden use of generative AI applications within organizations remains a significant hurdle to data sovereignty. Employees may copy sensitive data to generate insights and summaries without understanding the implications for compliance. Multiple techniques can be used to mitigate this risk:

• Enforce technical controls around what data is allowed as input into generative AI systems with the help of tools such as DLP solutions and CASB. This will help detect when employees are violating data policies.
• Offer secure alternatives so employees can enjoy generative AI’s benefits without compromising data security. For example, sensitive data may be disallowed for public generative AI systems but allowed in secure, localized versions.
• Publish acceptable use policies (AUPs) for generative AI that delineate what data can be input into generative AI systems and under what conditions.
• Regular employee training should be conducted so that employees become aware of the implications of bypassing security measures.

When generative AI use is properly governed, organizations can realize a range of benefits compared to shadow AI, as shown in figure 1.

Figure 1—Comparing Governed Generative AI with Shadow Generative AI

Extend Due Diligence to Generative AI Systems
Generative AI systems come in various models, each with its own set of controls and risk. Public generative AI models, such as ChatGPT, are the easiest to access via a software as a service (SaaS) systems, though these systems may levy the most compliance obstacles. As organizations explore these tools for their use cases, it is important to perform due diligence to evaluate and mitigate risk. Examples of potential risk-mitigating measures include:

• Data residency clauses—Specify where data will be processed and stored to ensure that data localization requirements are met.
• Data ownership and usage rights—Ensure that the organization owns input data and model output and does not reuse this information for model training.
• reach notification requirements—Require timely alerts for any data incidents involving organizational data.
• Right to audit and transparency obligations—Ensure that the generative AI vendor can demonstrate compliance with applicable data laws and provide visibility into data handling practices.

If a generative AI application cannot satisfy these baseline requirements, an organization should consider other, more secure alternatives, such as maintaining an in-house generative AI system. Many cloud providers offer enterprise-level generative AI platforms that can be deployed within isolated environments and trained on data that never leaves the organization’s infrastructure, helping to maintain strict adherence to data residency and sovereignty mandates.

Integrating Generative AI into Existing Control Frameworks
As generative AI becomes embedded in business operations, organizations face growing pressure to ensure that its use complies with evolving regulations and risk management principles. Organizations should begin by updating enterprise AI and data protection policies to explicitly address generative AI-specific concerns, particularly around data residency, localization requirements, model hosting, and vendor accountability.

Fortunately, most enterprises already operate under established IT governance structures that can be extended to address the unique challenges generative AI introduces, particularly concerning data sovereignty, cross-border data flows, transparency, and accountability.

Several regulatory and standard-based frameworks are shaping how organizations must govern AI:

• The EU AI Act⁷ is a historic regulation that classifies the use of AI on the basis of risk, ranging from minimal risk to prohibited use. This regulation requires organizations to impose stricter controls on high-risk applications. This includes mandatory impact assessments, transparency obligations, and post-deployment monitoring.
• The US National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF)⁸ outlines four core functions—Map, Measure, Manage, and Govern—to help organizations identify, assess, and mitigate risk across the AI life cycle. It emphasizes trustworthy AI through fairness, robustness, privacy, and accountability principles.
• Complementing these efforts is International Organization for Standardization (ISO)/International Electrotechnical Commission (IEC) 42001,⁹ the first international standard specifically designed for an AI management system. It provides a management system structure that organizations can use to ensure safe, responsible, and legally-compliant AI development and deployment.

Integrating generative AI principles into existing control frameworks—such as COBIT^®, ISO/IEC 27001, or NIST’s broader RMF—offers a practical path forward.¹⁰ A sample mapping is shown in figure 2.

Figure 2—Mapping Generative AI Controls to Common Frameworks

Establish a Cross-Functional AI Committee
Organizations should establish a multidisciplinary AI oversight group or working committee that includes stakeholders from legal, compliance, IT, cybersecurity, and data protection functions. Each of these groups plays a vital role. For example, the chief data officer (CDO) ensures that data classification, access, and localization rules are enforced across AI systems. The chief information security officer (CISO) incorporates generative AI controls into broader cybersecurity practices, focusing on access control, monitoring, and incident response.

Data Sovereignty as a Moving Target

Generative AI has ushered in a new era of innovation, but it also presents a paradigm shift in how organizations must think about data sovereignty and compliance. Unlike traditional technologies, generative AI systems process vast amounts of user input, generate unpredictable outputs, and operate across globally distributed infrastructures, all of which challenge conventional data residency and protection models.

For organizations, the path forward requires a proactive, strategic approach to governance:

• Understand where and how generative AI is used internally, including shadow AI tools.
• Classify and control the types of data input into or generated by generative AI systems.
• Work only with generative AI vendors who support data residency and privacy by design.
• Integrate generative AI into existing governance frameworks, assigning roles, policies, and continuous monitoring.

Ultimately, data sovereignty is becoming a core pillar of responsible generative AI adoption. Organizations that treat it as a central compliance concern, not an afterthought, will reduce regulatory risk and build greater trust with customers, regulators, and partners.

Endnotes

¹ Hu, K.; “ChatGPT Sets Record for Fastest-Growing User Base – Analyst Note,” Reuters, 2 February 2023
² Flinders, M.; Smalley, I.; “What Is Data Sovereignty?,” IBM, 3 June 2024
³ Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation [GDPR])
⁴ Ray, S.; “Samsung Bans ChatGPT Among Employees After Sensitive Code Leak,” Forbes, 2 May 2023
⁵ Flinders, M.; Smalley, I.; “What Is Sovereign Cloud?,” IBM, 8 May 2024
⁶ Amazon Web Services, “AWS GovCloud (US),”; Microsoft Azure, “Azure for US Government”
⁷ European Parliament, “EU AI Act: First Regulation on Artificial Intelligence,” 8 June 2023
⁸ National Institute of Standards and Technology (NIST), AI Risk Management Framework, Version 1.0, USA, January 2023
⁹ International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC), Joint Technical Committee on Artificial Intelligence (ISO/IEC JTC 1/SC 42), ISO/IEC 42001:2023 Information technology—Artificial intelligence—Management system, December 2023
¹⁰ ISO and IEC, Joint Technical Committee on Information Technology (ISO/IEC JTC 1), ISO/IEC 27001:2022 Information security, cybersecurity and privacy protection—Information security management systems—Requirements, Edition 3, 2022, ; ISACA, COBIT^®, USA, 2018; NIST, NIST Risk Management Framework (RMF), USA, 2016

APARNA ACHANTA

Is a security architect and leader at IBM Consulting with extensive experience driving mission-critical cybersecurity initiatives, particularly in federal agencies. Her leadership resulted in the establishment of security review boards, secure development practices, vendor evaluations, threat modeling, vulnerability management, code scanning, observability, and performance monitoring to ensure compliance with stringent federal guidelines. Achanta is also a member of the Forbes Technology Council, where she shares valuable insights on generative AI security challenges, and a founding member and speaker at the WomenTech Network. She was named to the list of 40 Under 40 in Cybersecurity by TopCyber News Magazine for her contributions to the cybersecurity community.