As AI technologies rapidly advance and integrate deeper into our daily and professional lives, one of the less obvious but critical compliance challenges concerns how AI providers use user-generated data to improve their models. Under the lens of the Copenhagen Compliance Dilemma, this raises essential questions around consent, transparency, and data stewardship, especially as AI’s capabilities to learn from user interactions evolve.
In this issue, we examine what compliance officers, legal teams, and digital governance professionals need to know about AI training practices, user consent rights, and the broader regulatory implications for data protection in an AI-driven world.
📌 How AI Models Learn From Your Data
AI models like ChatGPT, Sora, and other generative services improve over time through exposure to real-world data and interactions. This iterative learning process helps models:
- Become more accurate and context-aware.
- Improve problem-solving capabilities.
- Enhance overall safety and performance.
But where does this training data come from?
Often, it comes from users’ content shared during their interactions — with varying levels of consent and control.
📌 When and How Your Data Might Be Used
🔍 For individual services (ChatGPT, Sora, Operator):
- By default, content from your interactions may be used to train and improve AI models.
- Opt-out options are available, though not always clearly communicated. You can visit the provider’s privacy portal or product-specific settings to disable data use for training.
- Temporary chat features (like ChatGPT’s Temporary Chat mode) provide additional protection by ensuring interactions aren’t saved or used for model training.
🔍 For business services (ChatGPT Team, Enterprise, API):
- By default, business data is excluded from AI training.
- Organizations can choose to opt-in selectively, typically for product feedback or development collaborations.
- Enterprise customers should review their providers’ privacy and data-sharing policies regularly, as default settings and opt-in mechanisms may evolve over time.
📌 The Consent and Compliance Gap: A Growing Risk Area
The line between consented data use and implicit data harvesting is increasingly blurred in AI services. Recent regulatory scrutiny underscores the need for clearer user choices and fairer data practices:
📌 In 2024, Meta was fined €200 million by the European Commission under the Digital Markets Act (DMA) for its “consent or pay” model. Regulators concluded that this model deprived users of a genuine, freely given choice regarding the use of their personal data for personalized advertising.
📌 The AI Act in the EU and proposed updates to Singapore’s PDPA and UK’s AI Assurance Guidelines are introducing stricter transparency obligations for AI providers to disclose how user data is collected, stored, and used for training models.
📌 The EUGDPR Institute by Copenhagen Compliance Guidance: What Organisations and Users Should Do
✔️ Audit your AI service providers’ data training policies
- Ensure clear, documented understanding of what data is used for model improvement.
- Check for opt-in or opt-out default settings, especially after service updates.
✔️ Educate your teams and clients about data rights
- Inform users of their rights to disable model training or anonymize interactions.
- Highlight temporary or private interaction modes where available.
✔️ Strengthen enterprise data governance frameworks
- Ensure AI-related vendor contracts and data processing agreements specify data use for model training.
- Regularly review service-level agreements (SLAs) for compliance with GDPR, DMA, and other applicable regulations.
✔️ Monitor regulatory developments
- Stay informed on evolving global AI compliance standards under frameworks like the Copenhagen Compliance Global AI Standard Initiative.
- Prepare for mandatory disclosure requirements and audit trails related to AI training data usage.
📌 Final Thought: Trust is Built on Transparency
In an AI-driven economy, trust isn’t optional — it’s foundational. As AI systems increasingly learn from the content we generate, whether as individuals or organizations, it’s imperative to demand
The Copenhagen Compliance Dilemma reminds us: In rapidly advancing technological environments, regulating after the fact is often too late. Proactive, principle-based compliance — combined with transparent user choices — is the best safeguard for ethics, accountability, and public trust in AI.
The next online Data Protection Officer certification by EUGDPR Institute is on the 16-17th June