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Abstract
With increasing digitization in the banking sector, maintaining data confidentiality and managing access control have become critical concerns. This paper presents a novel hybrid Artificial Intelligence (AI) model integrating machine learning (ML) and rule-based systems to enhance data security in banking infrastructures. The model dynamically detects potential data breaches and enforces adaptive access protocols based on user behavior and risk scores. Comparative performance analysis with traditional access control systems shows marked improvements in breach detection, decision-making latency, and false positive rates.
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