## 3 Initial Results
#### 3.1.1 A Systematic Literature Review (SLR) of the Analyzed Concepts
Following the screening procedure identified that only 6 of the 45 articles fit the Boolean logic [14; 15; 16; 17; 18; 19], an outcome that highlights a significant research gap at the intersection of AI, blockchain, and sextortion, rather than being an inadequacy. This limitation of the study is thus acknowledged explicitly and justifies our dual review approach: a targeted SLR to identify what little work exists, in combination with a broader narrative exploration to capture adjacent insights, as presented in the following Section 3.1.2. The use of a narrative review, as informing a position paper, is recognized in emerging or interdisciplinary fields where empirical studies are few as a suitable means to synthesize and contextualize knowledge, as previously mentioned. Thus, although the quantitative aspect of the SLR is limited, our study remains comprehensive through the integrative strategy proposed in the methodological section presented previously. The main reasons for the exclusion of the rest of the articles were  
* Lack of AI or blockchain relevance (16 articles)
* No focus on digital abuse or sexual exploitation (12 articles)
* Absence of ethical considerations such as fairness, transparency, or data privacy (11 articles)  
We then coded the articles in Rayyan based on the following:  
* AI function (mitigation, surveillance, moderation, etc. ),
* Ethical focus (e.g., bias, explainability, misuse of data),
* Governance model (centralized vs. decentralized),
* Framework alignment (AI4 SG, resilience, digital trust). Considering that our review is exploratory and conceptual in nature, and our aim was to synthesize themes and inform a conceptual framework (rather than to evaluate an intervention's effectiveness), we did not assign formal \"quality scores\" to the 6 articles included in the study. All selected works are peer-reviewed and from reputable databases, as per the initial requirement, and were deemed directly relevant to our research question, through the process of inclusion/exclusion by the authors. This inherently sets a baseline of credibility, in line with guidance on integrative reviews, relying more on thematic insight than on formal quality scoring. Moreover, in view of the nature of this study as a position paper, the quantitative risk-of-bias appraisal would add limited value. However, for transparency, we have documented the qualitative coding protocol used in our review. Each of the 6 included studies was systematically coded along predefined dimensions: the role of AI (e.g., mitigation, detection), the ethical focus (fairness, privacy, etc. ), the governance model (centralized vs. decentralized), and alignment with broader frameworks (AI for Social Good, societal resilience). The authors independently applied these codes to each article, then compared and reconciled any differences to ensure reliability, both for the SLR and the narrative review. In Appendix A, we included the tables (Tables A1 and A2) for the coding protocols for each stage. We also included a summary table (Table A3) to how each study in the SLR seed 6 maps to these categories, providing a clear audit trail from literature to topics. This coding scheme, in line with established thematic analysis techniques  
A cluster map of the 6 articles [14; 15; 16; 17; 18; 19] considered for the systematic literature review is presented in Figure 3. However, the limited number of articles and the fact that the criteria for the intersection exclude a significant part of the overall picture of sextortion as a societal resilience threat in the context of AI and blockchain lead to the need for a more encompassing presentation of the concepts at hand. Thus, the methodological approach to include a narrative exploration is sound and offers the ability to cover the issue more comprehensively. Before presenting the narrative exploration, we chose to test whether the literature gap holds across a broader research landscape.