AI-Driven Data Governance Frameworks for Enhanced Privacy and Compliance
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While data is growing at an unprecedented rate and, at the same time, the necessary privacy standards are tightening, traditional approaches to data management no longer prove effective. The robust and integrated approach of AI-driven data governance presents a further opportunity to optimize some crucial processes, work in line with real-time regulation, and improve the data privacy measures. In this article, the author aims at presenting a broader view of how AI can be incorporated into the overall context of data governance, with an emphasis on automated classification of data, and anomalous pattern detection, dynamic/predictive policy execution, and data privacy enhancement tools such as differential privacy and Federated learning approaches.
The recent discussion outlines how AI frameworks are advantageous in enhancing efficiency by actively addressing risk and managing data lineage, and compatibility with new world-wide regulations including GDPR and CCPA. Several case studies in OPTIONS reveal that AI can address compliance requirements unique to industries such as healthcare, finance and e-commerce.
The strength of AI on the other hand sits hand in hand with its weaknesses such as ethical issues, reliance on automated systems and costly. Directions for the future, when explainable AI, blockchain or AI governance standardization will appear, are creating a basis for more stable AI systems. This article hence emphasizes the have to identify how decision-making organizations can embrace AI solutions that are scalable, creative but within the bounds of acceptable legal requirement in the ever changing data environment.
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