AI-Based Big Data Governance Frameworks for Secure and Compliant Data Processing
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I4P108Keywords:
Artificial Intelligence, Big Data Governance, Compliance, Data Security, GDPR, Data Privacy, Machine Learning, Anomaly DetectionAbstract
The technique to generate data at exponentially increasing rates due to the progress of cloud computing, social media platforms, and the Internet of Things (IoT) has moved us into a new era of big data. This data presents organizations with massive challenges in processing and ensuring secure processing in a GDPR, HIPAA, or CCPA-compliant manner. In large and complex modern-day data environments, old data governance mechanisms simply are not up to the task of dealing with both the volume, the variety, and the velocity of the current data world. A technology category that can empower us with the potential to transform big data governance is ‘Artificial Intelligence (AI),’ which is the ability to automate, adapt, and learn. In this paper, we present an in-depth study of the AI-based big data governance framework, which guarantees the processing of big data in a secure and compliant way. It discusses the integration of Machine Learning (ML), Natural Language Processing (NLP), and deep learning algorithms in the data governance processes. Finally, the paper proposes a multi-layered AI-driven framework that can automate data classification, ensure policy compliance, detect anomalies, and dynamically manage access to data. A literature review is made to pinpoint the various traditional approaches existing prior to 2020, outlining their limitations and the need for using intelligent governance models. The validation of the proposed methodology is performed through simulation, and the results show a drastic increase in compliance adherence and security metrics. Additionally, we examine the legal, ethical, and technical implications of deploying AI in governance. The results bear testimony to how critically important an investigational AI tool is to the secure, compliant, and hence useful creation of data ecosystems in the future
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