## 3 Initial Results
### Conceptual Framework: Integration Logic and System Architecture  
Building on the previous foundational blocks of AI and blockchain in the context of high-risk digital environments, we propose a conceptual framework that aligns the technical capacities of both technologies with broader societal, ethical, and governance considerations. This framework is meant to structure their interaction in a manner supporting trust, resilience, and regulatory compliance in AI applications. This design, anchored in decentralized infrastructure with an ethical angle (privacy-first), contributes to strengthen ing societal resilience against digital threats such as sextortion, and its main design logic is anchored in the dual role of the two technologies as depicted in Table 5 and visually presented in Figure 7.  
The integration of AI and blockchain into a unified conceptual framework strengthens technical resilience and also advances ethical governance through traceability, decentralization, and privacy-preserving mechanisms. This path was also considered by [67], who argued for the necessity of combining AI and blockchain to address the escalating gover  
Figure 7: A Privacy-First, Societal-Resilience-Oriented Conceptual Framework Integrating Blockchain and AI for Ethical Mitigation of Sextortion. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Data Privacy and Security** & **Ethical Concerns** & **Operational Risk** \\ \hline \multirow{2}{*}{Manifestation} & Unauthorized data access or & Bias, fairness, or lack of & System failure or misuse of \\  & breaches & transparency in AI systems & AI/Blockchain \\ \hline \multirow{2}{*}{AI Intervention} & Data encryption, secure & Transparent algorithm design, & Error detection and \\  & multi-party computation & fair training datasets & self-correction mechanisms \\ \hline \multirow{2}{*}{Blockchain Intervention} & Immutable ledger for & Blockchain for ensuring & Decentralized, fault-tolerant \\  & auditability, access control & transparency in & architecture, consensus \\  & via smart contracts & decision-making processes & mechanisms \\ \hline \multirow{2}{*}{Integrated Benefit} & Secure and verifiable data & Transparent, traceable, and & Robust and resilient system \\  & processing & fair decision-making & performance \\ \hline \multirow{2}{*}{Combined Impact} & Improved data protection & Enhanced accountability and & Minimized operational \\  & and user trust & fairness in AI outcomes & downtime and systemic risk \\ \hline \multirow{2}{*}{Example or Tool} & Privacy-preserving models, & Fairness metrics, decentralized & AI error detection tools, \\  & Blockchain-based consent & auditing systems & fault-tolerant consensus \\  & systems & systems & protocols \\ \hline \hline \end{tabular}
\end{table} 
Table 5: AI and Blockchain Risk and Resilience Framework. nance challenges posed by generative technologies. The integration is perceived, thus, as a structural mitigation measure or safeguard, and the CF-BIAI-SXT becomes a systems-level response to high-risk digital harms. Hence, the following characteristics: explainability, control, and institutional accountability, must be embedded by design. The framework has five core components (placed in the first layer in Figure 7):  
* Decentralization of Control--local governance, DAO logic, user data ownership. * Enhanced Security and Privacy--federated learning, ZKP, encryption. * Transparency and Accountability--audit trails, explainable AI, immutable logs. * Tech Bias Mitigation Strategy--data integrity, distributed validation. * Regulatory Compliance--GDPR-aligned design, consent tracking, legal risk. If, in the previous section, we referred to them in a mechanism-by-mechanism format, here we integrate them holistically, reassembling them in system-level integration logic.