Leveraging AI for Security and Compliance in Cloud-Native and Service-Oriented Architectures: A Framework Approach
Bibliographic record
Abstract
The increasing adoption of cloud-native and serviceoriented architectures (SOA) in modern enterprise systems introduces significant security and compliance challenges. These environments, characterized by their dynamic scaling, distributed services, and complex attack surfaces, necessitate innovative solutions to ensure robust protection and regulatory adherence. As organizations migrate to these architectures, they face an array of cybersecurity threats that require proactive and effective responses. This paper explores the potential of generative artificial intelligence (AI) and machine learning (ML) to enhance security and compliance in cloud-native and SOA ecosystems. It proposes a structured framework that integrates AI for real-time threat detection, adaptive defense, and automated compliance monitoring. This framework effectively addresses key security risks, such as dynamic workloads, cross-border data flows, and the growing complexity of regulatory requirements. Furthermore, it emphasizes maintaining legal and ethical standards through principles of AI transparency, fairness, and privacy-by-design, ensuring responsible AI deployment in these evolving environments.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".