Generative AI for Cybersecurity Awareness Training
Bibliographic record
Abstract
Human errors remain one of the leading causes of cybersecurity breaches during the rapid growth of cyber-attack techniques recently, underscoring the need for adaptive and personalized training programs. Traditional Cybersecurity Awareness Training (CSAT) programs often rely on static content, lack of personalization, and struggle remain in the face of evolving threats. This study presents an AI driven CSAT framework that integrates Generative Artificial Intelligence (GAI), Online Machine Learning, and adaptive data processing to deliver personalized, scalable, and real-time training contents. The proposed model utilizes GAI to dynamically generate interactive training contents tailored to individual user profiles based on job roles, behavioural risk indicators, impact assessment scores, and the pre-assessment outcomes. An online Naïve Bayes algorithm enables continuous risk profiling and content adaptation according to user performance, while windowing technique and integrated search engine ensure up-to-date and contextually relevant training materials. The proposed system aligns with Bloom’s Taxonomy and MITRE ATT&CK framework to ensure cognitive depth and real-world attack vector relevance. The research demonstrates that the integration of GAI with adaptive learning technologies significantly improves knowledge retention, enhance user engagement, and delivers cost-effective training solution. Further this study highlights the potential of GAI in revolutionizing cybersecurity training, ensuring resilience against evolving cyber-threats.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".