KOREAN ASSOCIATION FOR ARTIFICIAL INTELLIGENCE AND LAW
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KAAIL · AI LAW & POLICY RESEARCH

AI-Based Data Governance Framework for Enterprises in Cloud Environments: Design, Implementation, and Enterprise Evaluation

AI summary based on the abstract: This summary is based only on the abstract, and the full text was not reviewed. As a systematic literature review following PRISMA, it analyzed 67 peer-reviewed papers published from 2019–2026. The authors compared the limitations of rule-based governance with the potential of AI techniques and presented a reference architecture and maturity model

International ResearchCrossrefAI summary
Author

Masoom Peer Syed

Publication DateLanguage und

AI Research Summary

Before Human Review

Based on abstract · Full text not reviewed

This summary is based only on the abstract, and the full text was not reviewed. As a systematic literature review following PRISMA, it analyzed 67 peer-reviewed papers published from 2019–2026. The authors compared the limitations of rule-based governance with the potential of AI techniques and presented a reference architecture and maturity model.

Key Points from the Abstract

  1. Weaknesses of rule-based governance were identified as rigidity, lack of scalability, semantic ambiguity, slow policy adaptation, and weak anomaly detection.
  2. AI techniques such as machine learning, LLMs, reinforcement learning, and GNNs were reported to contribute to improved compliance detection accuracy and reduced manual work.
  3. A common architecture was derived consisting of ingestion-intelligence-policy/enforcement layers and policy-as-code, active metadata, and federated lineage.

Relevance to AI Law and Policy

Useful for organizing research trends in enterprise cloud data governance design and AI-based compliance automation.

Limitations to Consider

  • According to the abstract, legal and regulatory uncertainty, model drift, and multi-cloud consistency remain future challenges.
  • It is said that there is tension between accuracy and explainability, and between automation and auditability, but the detailed verification is insufficient based on the abstract alone.
  • Since the full paper was not reviewed, the detailed validity of the methods, data, and statistics cannot be confirmed.

Please compare with the original text before citing or using it in your assessment.

Abstract Preview
Challenges cloud-based enterprise data governance is encountering include data growth, regulatory changes, and the infle…
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Research Topic

AI Governance

Original text and source

Original Title

AI-Driven Data Governance Framework for Enterprises in Cloud Environments: Design, Implementation, and Enterprise Evaluation

The Korean title on this page was translated by AI.
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