Advanced International Journal for Research

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An Explainable AI-Driven Framework for Risk-Adaptive Security and Selective Auditing of Cloud Virtual Machines: Paper I—Explainable Regression-Based Selective Auditing

Author(s) Mr. Virendra Singh Thakur, Prof. Dipti Sharma
Country India
Abstract Cloud audit pipelines that apply every extended rule to every virtual-machine event can consume review capacity on low-priority activity. Purely statistical filtering, however, can omit resources that policy requires an organization to review. This paper presents ACB-RM-SA, a policy-first framework that combines an interpretable ridge-regression score for virtual-machine sensitivity with target-specific auditor suitability and deterministic mandatory-review gates. A reproducible synthetic evaluation generated 400 virtual-machine profiles, 120 identities and 60,000 access events using random seed 42. The ridge model was trained on 280 profiles and tested on 120 profiles, yielding MAE 0.0141, RMSE 0.0178 and R² 0.9807 against simulated assessor ratings. The high fit is expected because those ratings were produced from a declared, related linear policy with small interactions and noise; it is an internal consistency check rather than evidence of production predictive validity. At the capacity-driven threshold of 0.65, the framework selected 33,787 events (56.31%) for extended review and reduced modeled rule-evaluation work by 34.59%. In the 400-VM local batch benchmark, mean processing time decreased from 10.15 ms to 7.26 ms (28.49%). All 18,044 policy-critical events were retained by deterministic gating. This 100% figure is a policy-coverage property, not attack-detection recall. The results support interpretable prioritization while preserving explicit security obligations, but real-cloud telemetry and analyst validation remain necessary.
Keywords Keywords: Cloud Auditing, Virtual Machines, Ridge Regression, Least Privilege, Risk-Adaptive Access Control, Cloud Security
Field Engineering
Published In Volume 7, Issue 4, July-August 2026
Published On 2026-08-11
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