Generative AI and Cross-Border Data Transfers: Navigating Risk in a Fractured Regulatory Landscape

By 2027, 40% of AI-related data breaches will result from the misuse of generative AI across borders.

This Gartner prediction is a clarion call for privacy professionals everywhere. As businesses race to adopt generative AI (GenAI) tools to boost productivity and innovation, they often fail to anticipate the hidden risks that arise when data flows freely across jurisdictions with conflicting or immature regulatory frameworks.

In today’s digital arms race, where innovation outpaces regulation, the greatest challenge isn’t just what GenAI can do, but where and how it does it.

The new frontier: How GenAI has changed cross-border risk

The GenAI revolution isn’t confined to a single zip code. Modern AI systems rely on massive, diverse datasets that are routinely shuffled across borders for training, inference, and deployment. This global fluidity has introduced a potent cocktail of legal, operational, and ethical risks:

• Unintended data transfers: Employees using GenAI tools often have no idea where the data they’re entering is being stored or processed.
• Jurisdictional incompatibility: GDPR in Europe may mandate strict safeguards, while data processed in the U.S. could be subject to government surveillance under the CLOUD Act.
• Opaque vendor chains: When GenAI is embedded in SaaS tools, data may transit multiple subprocessors and locations, many outside corporate or regulatory oversight.

The risks are far from hypothetical. Italy’s data protection authority fined the U.S.-based developer of Replika €5 million for GDPR violations after the GenAI chatbot was deployed in Europe without sufficient transparency or legal basis. The case spotlighted how AI services developed in one jurisdiction can quickly clash with stricter privacy regimes abroad.

In short, generative AI turns every cross-border interaction into a potential privacy incident.

A patchwork of privacy laws: Why global inconsistency creates risk

Despite global calls for AI harmonization, the regulatory landscape remains fragmented:

• The EU’s AI Act enforces strict risk-based classifications and mandates transparency, human oversight, and data protection impact assessments.
• The U.S. approach remains largely sectoral and state-led, with inconsistent protections and few restrictions on cross-border data movement.
• APAC nations vary widely from China’s tight data localization laws to Singapore’s flexible but principled governance frameworks.

This regulatory dissonance forces organizations into a game of jurisdictional Jenga, where a single misplaced transfer could topple compliance.

GenAI and third-party risk: A perfect storm

AI has amplified third-party risk in every direction. According to EY, 87% of companies have faced third-party incidents in the last three years, yet nearly half still assess vendor risk only during onboarding. That’s a dangerous oversight in a world where:

• GenAI tools scrape and synthesize sensitive data.
• LLM APIs are embedded into apps and services without centralized visibility.
• Contractual language rarely accounts for data leakage via AI outputs.

Worse, many companies still rely on spreadsheets and static reports to manage AI-infused vendor ecosystems. That’s like navigating a hurricane with a paper map.

Beyond onboarding: AI-powered vendor risk demands constant vigilance

To manage AI-fueled third-party risk, privacy professionals must upgrade their playbook:

• Conduct continuous risk monitoring, not just onboarding assessments.
• Tier vendors by the criticality of their AI capabilities. Ask: Does this vendor use agentic AI? Is their model fine-tunable by default?
• Review transparency and explainability: Do the AI outputs make sense based on the inputs? Are they explainable and bias-tested?
• Demand disclosures about training datasets, system documentation, and known weaknesses.

As outlined in TrustArc’s Procurement Guide for AI Systems, embedding these expectations into your vendor due diligence process is essential.

Risk amplifiers: What makes GenAI especially volatile

• Re-identification: GenAI tools trained on aggregated or anonymized data can still reconstruct identifiable insights.
• Hallucinations: LLMs can fabricate facts about real individuals, creating privacy risks and reputational liabilities.
• Inference attacks: Malicious prompts can extract sensitive training data from GenAI models.
• Shadow AI: Employees using unauthorized tools introduce compliance blind spots.

Even when GenAI tools source public data, regulators are taking a closer look. In February 2025, Canada’s federal privacy commissioner launched an investigation into whether X (formerly Twitter) used personal data belonging to Canadians to train AI models without proper consent or legal justification.

This investigation underscores the legal uncertainty surrounding international AI training datasets and jurisdictional authority.

Add cross-border data flow to this equation, and the risk matrix escalates dramatically.

Strategies for mitigating cross-border GenAI risk

Privacy and compliance professionals aren’t powerless, but they must act with urgency. Here are key strategies:

1. Conduct Transfer Impact Assessments (TIAs)

Account for the legal environment of the destination country, especially if data is routed through GenAI APIs or services. Assess government surveillance risks, redress mechanisms, and vendor transparency.

2. Classify and control sensitive data

Implement role-based access, redact sensitive fields before AI ingestion, and label data that must not cross borders. PETs like data masking, tokenization, and synthetic data can help.

3. Update vendor due diligence for AI

Push beyond standard security checklists. Ask vendors:

• Where is data stored and processed?
• Are AI outputs monitored for leakage?
• What training data was used?
• Can you disable memory or retention features?

4. Operationalize AI acceptable use policies

Go beyond aspirational principles. Train staff on prohibited prompts, provide sanctioned tools, and monitor for policy violations. This should be a living policy, not shelfware.

5. Integrate AI into your privacy governance framework

Align with frameworks like the Nymity Privacy Management Accountability Framework. Incorporate GenAI oversight into data protection impact assessments (DPIAs), records of processing activities (ROPAs), and records of third-country transfers.

6. Establish AI governance committees

Bring together stakeholders across privacy, security, legal, and IT. Review use cases, monitor global developments, and guide responsible deployment across jurisdictions.

AI Impact Assessments: Your compliance crystal ball

AI Impact Assessments (AIIAs) are becoming a foundational tool for trustworthy AI governance. Inspired by DPIAs but tailored for GenAI, AIIAs help:

• Identify when an AI system poses heightened risks (e.g., automation of decisions with legal effects).
• Evaluate the training data, model architecture, and fairness measures.
• Analyze impacts on individuals, vulnerable populations, and social equity.
• Map risks to controls using frameworks like the NIST AI RMF or the EU AI Act.

TrustArc’s AI Risk Assessment Template is one example of how organizations can build structured evaluations aligned to global standards, from human oversight and system robustness to privacy-by-design safeguards.

By integrating AIIAs into procurement and deployment workflows, privacy leaders can move from reactive to predictive compliance.

The role of the privacy pro: From guardian to guide

In this fractured landscape, privacy professionals are risk reducers and strategic enablers. By embedding AI governance into the core of cross-border data strategy, they:

• Enable secure innovation.
• Build trust across markets.
• Future-proof compliance.

It’s a heavy lift, but privacy pros have carried heavier. Think of GenAI not as a rogue variable, but as your organization’s next great governance proving ground.

Moving from reaction to readiness in cross-border AI governance

As Gartner warns, cross-border GenAI misuse is no longer a fringe concern. It’s a ticking time bomb. Those who wait for global alignment will be left patching holes in their data governance after the fact.

To lead in the era of generative AI, organizations must:

• Embed privacy by design into all AI initiatives.
• Treat every data transfer as a risk vector.
• Centralize visibility into GenAI use across the enterprise.

Global complexity isn’t going away. But with the right strategies, privacy leaders can meet it head-on, not just with caution, but with confidence.