Machine Learning-Based Cloud Computing Compliance Process Automation

Yuqing Wang

Abstract:

Cloud computing adoption across industries has revolutionized enterprise
operations while introducing significant challenges in compliance management.
Organizations must continuously meet evolving regulatory requirements such as GDPR and
ISO 27001, yet traditional manual review processes have become increasingly inadequate
for modern business scales. This paper presents a novel machine learning-based framework
for automating cloud computing compliance processes, addressing critical challenges
including resource-intensive manual reviews, extended compliance cycles, and delayed risk
identification. Our proposed framework integrates multiple machine learning technologies,
including BERT-based document processing (94.5% accuracy), One-Class SVM for
anomaly detection (88.7% accuracy), and an improved CNN-LSTM architecture for
sequential compliance data analysis (90.2% accuracy). Implementation results demonstrate
significant improvements: reducing compliance process duration from 7 days to 1.5 days,
improving accuracy from 78% to 93% and decreasing manual effort by 73.3%. Real-world
deployment at a major securities firm validated these results, processing 800,000 daily
transactions with 94.2% accuracy in risk identification.