International Journal on Science and Technology

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AI-Orchestrated Regulatory Twin: A Self-Adaptive Compliance Mirror for Cloud-Native Data Systems

Author(s) Sai Kishore Chintakindhi
Country United States
Abstract This dissertation explores the creation of an AI regulatory twin, intended as a self-adjusting compliance tool for cloud-native data systems, particularly focusing on the persistent problem of keeping up with rapidly changing digital regulations. The research uses a combined method, bringing together data from current regulatory systems, complex cloud structure details, compliance measurements, and up-to-date operational data from cloud setups. Results show that the AI regulatory twin is able to effectively design and improve compliance plans. It achieves this using an adaptive algorithm which links regulatory needs to real-time operational adjustments, leading to around a 30% rise in compliance levels within the health data systems tested. These results are especially meaningful for the health sector, where managing sensitive information is key to patient well-being and smooth operations. By helping healthcare groups achieve current compliance, this study not only improves adherence to regulations but also encourages confidence in cloud infrastructures, helping to develop new digital health answers. The possible effects go past just healthcare. It introduces a changing framework that could shape regulatory actions across different industries depending on cloud tech and support a safer and more compliant digital world.
Field Engineering
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-05-16
DOI https://doi.org/10.71097/IJSAT.v16.i2.5269
Short DOI https://doi.org/g9kc5j
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