Introduction
As artificial intelligence becomes more embedded in business operations, organisations increasingly rely on third-party AI service providers. This reliance raises important questions about data processing agreements (DPAs) and, crucially, the geographic location of your business data during AI processing. Knowing where your data is processed is not merely a technical detail; it is a legal, regulatory, and security imperative.
The Importance of Data Location in AI Processing
Data processing is rarely a straightforward, single-location operation. AI systems often leverage distributed cloud infrastructure, sometimes spanning multiple countries or continents. This distributed nature poses a challenge in understanding exactly where your data is held, processed or analysed. From a regulatory perspective, the location of data processing influences compliance with data protection laws such as the UK GDPR, the EU GDPR, and other jurisdictional rules.
Data sovereignty concerns also come into play. Countries differ in their legal requirements about data access and transfer, impacting the confidentiality and integrity of your business data. When AI providers use cloud services hosted in multiple jurisdictions, the risk landscape becomes more complex.
Key Considerations for AI Data Processing Agreements
1. Explicit Clauses on Data Location
DPAs should clearly specify where data will be processed and stored. Ambiguous language like "data may be processed outside the UK" without detailed explanations is insufficient. Organisations must demand precision about processing jurisdictions, including the physical locations of data centres.
2. Cross-border Data Transfers
International data transfers are subject to rigorous controls under UK GDPR and EU law. When AI providers process data outside the UK or EEA, adequate safeguards must be in place. These could include Standard Contractual Clauses (SCCs), adequacy decisions, or Binding Corporate Rules (BCRs). Your DPA should clarify which mechanisms are used to legitimise such transfers.
3. Sub-processor Transparency and Control
AI providers often employ sub-processors to manage various aspects of data handling. Organisations should insist on transparency regarding these sub-processors and retain control over when and how third parties process the data. DPAs should include procedures for notifying clients and obtaining consent for new sub-processors.
4. Security Measures Aligned with Data Location Risks
Security requirements must reflect the risks linked to the data location. Some jurisdictions may present higher risks regarding government access or cyber threats. DPAs must specify security standards - such as encryption, access controls, and monitoring - tailored to these risks, ensuring adequate protection wherever data is processed.
Challenges Specific to AI Data Processing
AI systems introduce unique complexities beyond typical data processing:
- Data Replication and Backup: AI workloads often involve replicating large datasets across multiple nodes for performance and reliability. This replication must be addressed explicitly in agreements to avoid unintended exposure in jurisdictions not covered by your compliance framework.
- Model Training and Inference Locations: The training of AI models may occur in one location, while live inference (i.e., real-time data processing) happens elsewhere. Both locations need to be transparent in the DPA.
- Third-party Cloud Providers: AI applications commonly rely on large cloud providers with global infrastructures. Understanding how these providers manage data localisation and compliance is essential.
Practical Steps for Businesses
To safeguard your business data in AI deployments, consider the following approach:
- Conduct Due Diligence: Evaluate your AI providers’ infrastructure, processes, and compliance posture regarding data location.
- Negotiate Precise DPAs: Avoid generic templates. Insist on clauses that reflect your regulatory environment and risk appetite.
- Engage Legal and Technical Experts: Ensure agreements are reviewed for regulatory compliance and technical feasibility.
- Monitor and Audit: Establish ongoing oversight mechanisms to verify that processing locations and practices remain compliant.
- Plan for Incident Response: Understand the implications of data breaches or regulatory investigations in various jurisdictions and incorporate response plans in your contracts.
Conclusion
In the evolving landscape of AI and data protection, knowing where your business data is truly processed is paramount. Data processing agreements must move beyond boilerplate wording to explicitly define processing locations, controls, and legal safeguards. A rigorous, informed approach enables organisations to harness AI technologies confidently, while maintaining compliance and protecting their data assets.
Richard J. Keenlyside
Fractional CIO/CTO/CISO - UK-based technology leadership with over 25 years’ experience