AI-Driven Zero-Trust Cloud Security: Automated Threat Response Leveraging Multi-Cloud Data Lakes and LLMS
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.001 | 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