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AI-Driven Compliance Automation for GDPR and Cross-Border Data Protection Laws: Challenges and Opportunities
编号：92 访问权限：仅限参会人 更新：2026-07-27 05:14:16 浏览：14次 Online

报告开始：2026年07月31日 14:40（Asia/Kolkata）

报告时间：15min

所在会场：[S6] Artificial Intelligence Use Cases [S6-7] Artificial Intelligence Use Cases

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摘要
The expansion of rigorous data protection standards, such as the European Union's General Data Protection Regulation (GDPR) and a complicated framework of cross-border data transfer protocols, has placed considerable operational and financial strains on enterprises globally. People increasingly view manual compliance procedures as inefficient, prone to errors, and lacking in scalability. Thus, Artificial Intelligence (AI) and Machine Learning (ML) are emerging as revolutionary technologies for automating compliance functions. This paper offers an in-depth examination of the opportunities and problems associated with AI-driven compliance automation. We examine the potential of Natural Language Processing (NLP) to automate the management of data subject access requests (DSAR), the capability of machine learning models to classify and identify sensitive material, and the role of artificial intelligence in monitoring data flows for compliance with cross-border transfer regulations. The implementation of these systems is beset by problems, such as the opaque nature of sophisticated models, algorithmic bias resulting in discriminatory compliance outcomes, the evolving nature of legal interpretations, and the substantial resource demands for execution. This work conducts a thorough literature review to summarize existing knowledge, identify significant gaps, and propose a future research agenda. We conclude that although AI presents a transformative opportunity for proactive and scalable compliance, its effective integration requires a human-in-the-loop methodology, stringent model governance, and interdisciplinary collaboration among legal professionals, data scientists, and policymakers to guarantee that compliance automation does not undermine fundamental rights and legal integrity.
关键词
Cybersecurity, Deep Learning, Ethical AI, Intrusion Detection Systems, Grey Wolf Optimizer, Multi-Objective Optimization, Privacy Preservation.
报告人
Ababneh Jafar
phd Zarqa University

稿件作者
Hattar Hattar Zarqa University
jafar ababneh Jafar Ababneh Cyber Security department； Faculty of Information Technology Zarqa University Zarqa； Jordan jababneh@zu.edu.jo
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重要日期

  • 会议日期

    07月30日

    2026

    至

    08月01日

    2026

  • 07月28日 2026

    注册截止日期

  • 07月30日 2026

    初稿截稿日期

主办单位
The United Societies of Science
承办单位
Kongunadu College of Engineering and Technology
协办单位
IEEE Section
IEEE Madras Section
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