Training data for AI models raises complex intellectual property and contractual issues beyond personal data concerns. Projects must verify they possess proper rights to use all data feeding their algorithms. Scraping online content without permission could violate website terms of service or copyright protections, particularly for non-factual creative works. AI-token ventures should conduct comprehensive IP audits of training data sources and secure appropriate licenses for proprietary information. Some projects mitigate this risk by using only open data or content contributed by users with explicit permission, often incentivized through token rewards. Additionally, outputs generated by AI systems may themselves create new IP issues, requiring clear policies on ownership and usage rights. Legal opinions must analyze these training data sources and recommend appropriate licensing structures to avoid infringement claims.

Cross-border data transfers add another layer of complexity for globally distributed AI token systems. The GDPR restricts transferring personal data to jurisdictions lacking “adequate” protection, creating challenges for blockchain networks operating across multiple countries. A project with nodes processing European user data in non-EU countries must implement appropriate safeguards, such as Standard Contractual Clauses or Binding Corporate Rules. The decentralized nature of blockchain technology complicates this requirement, as personal data stored on-chain could potentially be accessed by nodes worldwide. Projects must carefully design their architecture to either keep personal data off-chain or implement encryption and privacy-preserving techniques for on-chain storage. Legal opinions should address these cross-border transfers and recommend compliant technical measures for global operations.

The privacy-by-design principles represent best practices that increasingly appear in regulatory requirements. AI-token projects should minimize data collection to necessary elements, implement data anonymization or pseudonymization where possible, and build privacy protections directly into technical architecture. Conducting a Data Protection Impact Assessment (DPIA) provides a structured methodology for identifying and mitigating privacy risks before launch. This assessment becomes mandatory under GDPR for high-risk processing activities like large-scale profiling or biometric data collection. The assessment should document all data flows, processing purposes, security measures, and potential risks to user rights. A thorough DPIA demonstrates regulatory compliance and often reveals potential vulnerabilities that can be addressed before they become liabilities. Legal opinions should include an evaluation of whether the project requires a formal DPIA under applicable regulations and provide guidance on implementing privacy-by-design measures.