Application of Machine Learning to Determine Cybersecurity Compliance: Analyze the Risk of Privacy Violations in Distributed Systems
Bibliographic record
Abstract
Distributed computing environments grow exponentially – encompassing cloud, edge, and IoT ecosystems. This trend leads to near-unbeatable complexity in cybersecurity compliance management. Organizations deploying distributed systems around cloud, edge, and IoT for scalability and real-time operons find it more and more complicated to guarantee that systems comply with privacy regulations, including GDPR, HIPAA, CCPA, and CL In distributed architectures, manual auditing, and other rule-based options fell short on detecting emerging threats or concealed risks, as well as delicate deviations from a policy across a vast number of nodes. ML-based techniques could automate and significantly improve the process of ensuring cybersecurity compliance and analyzing privacy risks in completive distributed mechanisms. The approach described in this paper relies upon supervised and unsupervised ML models considering patterns of system operations, user accesses, and configurations across distributed environments. Thorough registered training on compliance, the models classify potential privacy violatio9ns, measure the alignment with compliance policies, and generate futuristic predictions on the matter of non-compliance. Functionalities like dynamic profiling, real-time correlations, and anomaly detection assess a system’s compliance posture, prioritizing most relevant and severe risks. The explainability modules have been included, allowing interpreting system decisions and facilitating human-in-the-loop decisions. The developed approach has been assessed in experiments with hybrid datasets containing synthetic and real-life logs from the multicloud scenarios. According to the results, the approach demonstrates high accuracy of classification, robust anomaly detection, and the possibility of early risk detection, largely exceeding the performance of traditional rule-based systems. The system also required minor adjustments to comply with novel rules, making it compatible for organizations facing dynamic legislative environments. All-in-all, this paper confirms the hypothesis that compliant analysis powered by ML can bridge the gap between static policy documents and dynamic distributed architectures. Making data produce actionable insights on compliance not only improves cybersecurity postures but also allows stakeholders to report to regulators and control risks more efficiently. The research provided a basis for future work in autonomous compliance systems development and privacy-aware AI governance frameworks.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".