Artificial Intelligence and Information Governance: Strengthening Global Security, through Compliance Frameworks, and Data Security
Bibliographic record
Abstract
This study examines the dual role of artificial intelligence (AI) in advancing and challenging global information governance and data security. By leveraging methodologies such as Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), Structural Equation Modeling (SEM), and Multi-Criteria Decision Analysis (MCDA), the study investigates AI-specific vulnerabilities, governance gaps, and the effectiveness of compliance frameworks. Data from the MITRE ATT&CK Framework, AI Incident Database, Global Cybersecurity Index (GCI), and National Vulnerability Database (NVD) form the empirical foundation for this analysis. Key findings reveal that AI-driven data breaches exhibit the highest regulatory scores (0.72) and dependency levels (0.81), underscoring the critical need for robust compliance frameworks in high-risk AI environments. PCA identifies regulatory gaps (45.3% variance) and AI technology type (30.2% variance) as significant factors influencing security outcomes. SEM highlights governance strength as a primary determinant of security effectiveness (coefficient = 0.68, p < 0.001), while MCDA underscores the importance of adaptability in governance frameworks for addressing AI-specific threats. The study recommends adopting quantum-resistant encryption, enhancing international cooperation, and integrating AI automation with human oversight to fortify governance structures. These insights provide actionable strategies for policymakers, industry leaders, and researchers to navigate the complexities of AI governance and align technological advancements with ethical and security imperatives in a rapidly evolving digital landscape.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".