riskAIchain: AI-Driven IT Infrastructure—Blockchain-Backed Approach for Enhanced Risk Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the evolving landscape of cybersecurity, traditional information technology (IT) infrastructures often struggle to meet the demands of modern risk management frameworks, which require enhanced security, scalability, and analytical capabilities. This paper proposes a novel artificial intelligence (AI)–driven IT infrastructure backed by blockchain technology, specifically designed to optimize risk management processes in diverse organizational environments. By leveraging artificial intelligence for predictive analytics, anomaly detection, and data-driven decision-making, combined with blockchain’s secure and immutable ledger for data integrity and transparency, the proposed infrastructure offers a robust solution to existing challenges in risk management. The infrastructure is adaptable and scalable to support a variety of risk management methodologies, providing a more secure, efficient, and intelligent system. The findings highlight significant improvements in the accuracy, speed, and reliability of risk management, underscoring the infrastructure’s capability to proactively address emerging cyber threats. To ensure the proposed model effectively addresses the most critical issues, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique will be used to analyze and evaluate the interrelationships among the existing critical factors. This approach evaluates the interrelationships and impacts of these factors, verifying the model’s comprehensiveness in managing organizational risk. This study lays the foundation for future research aimed at refining AI-driven infrastructures and exploring their broader applications in enhancing organizational cybersecurity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it