## 3 Initial Results
#### 3.1.2 A Narrative Conceptual Exploration  
The SLR in the previous section maps the limited empirical footprint at the intersection of AI, blockchain, and sextortion. This section, working on those findings, adopts a broader epistemic lens, engaging with adjacent topics to build towards the conceptual framework. This shift allows for the connection of the technical architectures with ethical governance and societal resilience perspectives, critical to the design logic of CF-BIAI-SXT. To ensure traceability of this narrative review, we used a modified coding sheet, similar to the one used for the SLR, to map every narrative sources considered and referenced. This coding sheet is presented in Table 1 in Appendix A, to allow external researchers to follow and replicate or extend our thematic decisions, although it was not meant to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Thematic Area** & **Subtopic** & **Key Concepts/Mechanisms** & **Representative Studies** \\ \hline Blockchain-AI & Privacy-preserving & Zero-knowledge proof, & [20; 21; 22] \\ Integration & frameworks & Homomorphic encryption, etc. & [20; 21; 22] \\ \hline  & Decentralized governance & Smart contracts, Identity & [23; 24] \\  & & management, Incentive distribution & [25; 26] \\ \hline  & Technical architectures & Hyperledger Fabric, Ethereum, & [27; 28] \\  & & Reinforcement learning & [25; 26; 27] \\ \hline Ethical Frameworks & Governance mechanisms & Balanced approaches, smart & \\  & & contract-based governance & [28; 29] \\ \hline  & Transparency protocols & Smart contracts for ethics & \\  & & enforcement & [24; 30; 31] \\ \hline  & Privacy safeguards & Audit tracking, encryption, and & \\  & & de-identification & [32] \\ \hline Sextortion Mitigation & Detection, Verification, & AI detection, blockchain & \\  & Victim protocols & verification & \\ \hline Societal Resilience & Stakeholder coordination & Cross-org data sharing, governance & \\  & & trust systems & [3; 29] \\ \hline  & System effectiveness & Infrastructure performance & [27; 33] \\ \hline  & Social impact & Equity, public trust, fairness & [29; 34] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: **Themes in Blockchain, AI and Sextortion Research. **eliminate subjectivity. The snowball method with added reference points based on the concepts yielded a total number of 157 sources, of which 118 peer-reviewed and 39 non-peer-reviewed (news reports, policy/brief items, arXiv/working papers, or other gray-lit, such as the FBI web brief, OECD framework). These 157 sources include the 6 seed sources from the SLR and are listed in full in the references list. The corpus breakdown is as follows: 27% refer to digital-abuse technology, 19% to AI dual-use and risk, 22% to Ethical AI frameworks, 20% to blockchain capacity, and 12% to societal resilience. Their direct informing of the framework is detailed in the narrative review in this section, mostly in tabular format, with other sources linked indirectly being mentioned in other parts of the article. The corpus also includes survivor-voice studies, such as [1; 35] that provide first-person evidence on coercion dynamics and thus, lead to informing the data-wallet consent logic of CF-BIAI-SXT. The conceptualization of sextortion mitigation as embedded in a multilayered ethical ecosystem that includes AI for social good, blockchain-enabled governance, societal resilience, and supporting regulatory and equity frameworks is achieved in a three-step process. Firstly, we identify the digital technologies that provide the environment for sexual exploitation leading to sextortion. Sextortion, defined as a form of sexual exploitation in which victims are, e.g., extorted with their sexual images [36], is a form of dissolution of societal resilience. It exploits vulnerabilities in social and economic structures and corrodes the integrity of society by breaking down structures meant to protect privacy and security.