Advancement in AI driven Security Models for safeguarding Financial Data
Year: 2024
Type: article
Abstract: The rapid advancement of Artificial Intelligence (AI) has significantly influenced the security of big data systems. This article explores the current state-of-the-art methodologies, applications, and challenges associated with AI-driven security enhancements in big data environments. AI techniques, including machine learning, deep learning, and natural language processing, are being leveraged to detect cyber threats, automate anomaly detection, and improve data protection mechanisms. The study highlights AI's role in securing large-scale datasets by analyzing vast amounts of information in real-time, identifying potential vulnerabilities, and mitigating cyber risks proactively. Various AI-powered frameworks and models employed in cybersecurity are examined, with a focus on their effectiveness in addressing evolving cyber threats. The article also discusses the integration of AI in encryption techniques, behavioral analytics, and automated response mechanisms. Despite the numerous benefits AI brings to big data security, challenges such as data privacy concerns, algorithm bias, and adversarial attacks persist. This article provides insights into ongoing research and potential solutions to enhance AI-driven security frameworks. Finally, the study underscores future research directions, emphasizing the need for robust AI governance, ethical AI deployment, and regulatory compliance to ensure the secure management of big data systems.
Author: Prashant Awasthi
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6930812930
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