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In the swiftly transforming terrain of Artificial Intelligence (AI), secure data integrity has emerged as a critical priority. This analysis delves into the latest breakthroughs, trends, and hurdles at the convergence of data protection and AI innovations. Regulatory frameworks such as the General Data Protection Regulation (GDPR) and technological advancements like federated learning are scrutinized for their influence on privacy safeguarding and adherence to regulations. Ethical dimensions, encompassing equity and transparency within AI algorithms, are also brought to light. The amalgamation of empirical evidence and theoretical perspectives offers a thorough snapshot of contemporary advancements and anticipated pathways in AI-centric data governance.

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Innovations in Data Security and Intelligent Automation through Artificial Intelligence: Patterns and Hurdles

  • 章节

  • 发表日期：

    2026年

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  • Rajeev Cherakkara Veedu,
  • Srinath Doss,
  • Kamal Kant Hiran

摘要

In the swiftly transforming terrain of Artificial Intelligence (AI), secure data integrity has emerged as a critical priority. This analysis delves into the latest breakthroughs, trends, and hurdles at the convergence of data protection and AI innovations. Regulatory frameworks such as the General Data Protection Regulation (GDPR) and technological advancements like federated learning are scrutinized for their influence on privacy safeguarding and adherence to regulations. Ethical dimensions, encompassing equity and transparency within AI algorithms, are also brought to light. The amalgamation of empirical evidence and theoretical perspectives offers a thorough snapshot of contemporary advancements and anticipated pathways in AI-centric data governance.