用机器学习自动检查云合规，提速降本增准。
Machine Learning-Based Cloud Computing Compliance Process Automation
- 融合BERT、One-Class SVM和CNN-LSTM，智能处理合规文档与数据
- 合规周期从7天缩短至1.5天，准确率提升至93%
- 适合金融、医疗等强监管行业的自动化合规需求
云计算在各行业广泛应用，推动企业运营变革，但合规管理面临巨大挑战。组织需持续满足GDPR、ISO 27001等不断演进的监管要求，传统人工审查已难适应现代业务规模。本文提出一种基于机器学习的云合规流程自动化框架，解决人力密集、周期长、风险发现滞后等问题。该框架集成多种机器学习技术：基于BERT的文档处理准确率达94.5%，One-Class SVM异常检测准确率为88.7%，改进的CNN-LSTM架构对序列合规数据的分析准确率达90.2%。实施结果显示，合规流程时长由7天缩减至1.5天，准确率从78%提升至93%，人工工作量减少73.3%。在一家大型证券公司的实际部署中，系统每日处理80万笔交易，风险识别准确率达94.2%。
原文摘要 · Abstract (English)
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%. A real-world deployment at a major securities firm validated these results, processing 800,000 daily transactions with 94.2% accuracy in risk identification.
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