## 3 Initial Results
### Regulatory and Legal Harmonization  
The proposed framework, which is mainly technical, must first be anchored in legal reality, and this sub-section explains how it fits into the real-world regulatory fabric--GDPR, AI Act, cross-border data, etc. Given the divergence between the two technologies and the resulting regulatory tensions highlighted in Table 6, specific challenges must be taken into account for effective deployment of blockchain-integrated AI systems [137]. \begin{table}
\begin{tabular}{c c c c} \hline
**Issue** & **AI Systems** & **Blockchain Systems** & **Resulting Conflict** \\ \hline \multirow{2}{*}{Data deletion (Right to  Erasure)} & Requires the ability to & \multirow{2}{*}{Immutability prevents data (GDPR, AI Act)} & \multirow{2}{*}{Contradicts GDPR’s “right to  deletion once recorded} & \multirow{2}{*}{Contradicts GDPR’s “right to  be forgotten”} \\  & delete or modify user data & & \\  & (GDPR, AI Act) & & \\ \hline \multirow{2}{*}{Data controller  accountability} & Centralized or joint & \multirow{2}{*}{Distributed nodes make  accountability attribution  difficult} & \multirow{2}{*}{Legal uncertainty over  controller responsibility} \\  & controllers are usually & & \\  & identifiable & & \\ \hline \multirow{3}{*}{Data minimization} & Requires minimal, & \multirow{3}{*}{AI often relies on large,  central datasets} & \multirow{3}{*}{Conflicts with blockchain’s preference for minimal storage} \\  & purpose-bound data & & \\ \cline{1-1}  & collection & & \\ \hline \multirow{3}{*}{Transparency and  auditability} & Emphasis on explainable  models and traceability & \multirow{3}{*}{Emphasis on immutable,  transparent records} & \multirow{3}{*}{Overlap exists, but also  tension when AI uses opaque  models} \\  & & & \\ \hline \multirow{3}{*}{Privacy protection (data  sensitivity)} & Strong emphasis on  safeguarding personal data & \multirow{3}{*}{Blockchain makes all  transactions visible unless  privacy-enhancing tech is } & \multirow{3}{*}{Conflict unless  privacy-preserving tech (ZKP,  homomorphic encryption) is  used} & \multirow{3}{*}{Privacy-preserving tech (ZKP,  homomorphic encryption) is  added} \\  & Subject to & \multirow{3}{*}{Blockchain networks  Regulatory compliance is \(\,\)} \\  & jurisdiction-specific rules & & \\ \cline{1-1}  & (GDPR, CPRA, etc.) & & \\ \hline \end{tabular}
\end{table} 
Table 6: Key Legal Tensions between AI and Blockchain Regulatory Logics. The reconceptualization of governance models requires aligning technical systems with legal and institutional architectures, as also shown by [138] in a context of policing of cybercrime. This is even more valid in the context of decentralized AI-blockchain systems, which challenge conventional accountability frameworks. However, there are numerous discrepancies and regulatory gaps, making the entire process of alignment extremely difficult and often bound for failure. One possible solution is the adoption of so-called regulatory sandboxes [139], providing a controlled environment for the testing of innovative blockchain-based AI systems in the context of relaxed regulations [140], and allowing for interdisciplinary collaboration to identify compliance issues and refine frameworks in real-world scenarios [141]. These sandboxes may be created under national data protection authorities or blockchain consortia, if supranational entities, such as the European Commission, fail to deploy them due to bureaucratic aspects. In parallel, self-regulatory principles aligned with frameworks like the OECD AI Principles and EU Blockchain Strategy offer an interim solution, enabling industry stakeholders to set governance standards while awaiting formal regulations [102]. Furthermore, cross-border regulatory divergence--particularly between the GDPR in the EU and the sectoral approach in the US, which proposes regulations for each type of the following: healthcare, finance, children's privacy rights, and education--complicates compliance for decentralized AI systems.