AI-Driven Zero-Trust Cloud Security: Automated Threat Response Leveraging Multi-Cloud Data Lakes and LLMS
Bibliographic record
Abstract
Zero-Trust architectures have become the foundation of modern enterprise security, requiring continuous authentication, least-privilege enforcement, and pervasive monitoring. However, as organizations increasingly adopt multi-cloud infrastructures, traditional Zero-Trust implementations struggle with scale, data silos, and evolving adversarial tactics. This paper explores how artificial intelligence (AI) and large language models (LLMs) can enhance Zero-Trust principles by automating threat detection and response across multi-cloud data lakes. We propose an integrated architecture where multi-modal telemetry feeds AI-driven analytics pipelines, producing explainable, automated security actions that reduce analyst fatigue while strengthening compliance. By leveraging LLMs for context enrichment and response orchestration, enterprises can operationalize Zero Trust at scale, aligning automation with trustworthiness. Case studies, experimental results, and analyst-centric explainability approaches demonstrate that AI-enhanced Zero-Trust is not only feasible but necessary for defending against increasingly sophisticated threats.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".