Ai-Driven Cybersecurity Awareness Training (CSAT) Framework
Bibliographic record
Abstract
Human error continues to be a prevailing cause of cybersecurity attacks, and it necessitates immediate responsive and personal training solutions. Conventional Cybersecurity Awareness Training (CSAT) schemes are generally contextually irrelevant, lack scalability, and are missing ongoing risk assessment. The paper will mitigate such shortcomings by suggesting a multi-phase CSAT model whereby Generative Artificial Intelligence (GAI), online machine learning algorithms, and MITRE ATT&CK- based threat models are incorporated to provide rolespecific and threat-sensitive delivery of cybersecurity education. The framework includes realtime user profiling, vulnerability analysis and a machine learning pipeline that includes Random Forest with Recursive Feature Elimination, Isolation Forest, K-Means clustering and Decision Trees to find out risks based on behavior. The dynamically generated training content is based on weaknesses that are unique to the users and the changing risk profiles between the users. Simulated organizational profiles with experimental evaluation indicate that there were significant changes of Risk Scores and Vulnerability Scores among partici-ants. This confirms the model as effective in increasing the awareness of the users, alleviating behavioral weaknesses, and facilitating ongoing learning. The suggested framework provides flexible and smart approach to reinforcing organizational cybersecurity postures and providing quantifiable and specific outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".