Bibliographic record
Abstract
Purpose This study aims to identify the key artificial intelligence (AI) skills in demand for cybersecurity roles globally and examines their relationships with cybersecurity tasks across different countries. It aims to address the knowledge gap in AI skill requirements and how they vary regionally to inform workforce development and educational programs. Design/methodology/approach Using semantic network analysis (SNA), the study analyzes 8,262 job postings from nine countries, including the USA, UK, UAE, France, Germany, Canada, Belgium, Australia and Italy. Data was collected from Indeed.com using a Python tool, followed by text preprocessing and network mapping of AI skills. Findings The analysis shows that AI skills such as machine learning (ML), natural language processing (NLP), predictive analytics and neural networks are in high demand globally. These skills are closely tied to cybersecurity functions such as threat intelligence, anomaly detection and automated incident response. Regional differences exist, with the USA and UK focusing on threat intelligence, while the UAE emphasizes automated incident response. Research limitations/implications The study is limited to job postings from Indeed.com. Expanding to other job platforms and regions would provide a broader perspective. The subjective interpretation of SNA may also introduce bias in identifying skill relationships. Practical implications Educational institutions, job seekers and employers can use these findings to tailor curricula, job descriptions and training programs, addressing the most critical AI skills in cybersecurity. Originality/value To the best of the author’s knowledge, this study is among the first to use SNA to map global AI skills demand in cybersecurity, offering valuable cross-country insights that fill a critical research gap.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".