(untitled) (International Journal of Advanced Research in Computer Science & Technology(IJARCST), 2023)
The rapid digital transformation of enterprises has significantly increased the complexity of cybersecurity, regulatory compliance, financial governance, and operational management. Traditional security systems often operate in isolation, making it difficult for organizations to detect sophisticated cyber threats, maintain continuous regulatory compliance, and optimize business processes. Artificial Intelligence (AI) has emerged as a transformative technology capable of integrating these critical functions into a unified enterprise platform. This study explores the concept of an AI-driven enterprise security platform that combines adaptive compliance, financial intelligence, and autonomous operations to enhance organizational resilience and decision-making. The proposed framework employs machine learning, deep learning, natural language processing, predictive analytics, and intelligent automation to continuously monitor security events, identify compliance risks, detect financial anomalies, and automate operational workflows. The integration of AI enables organizations to respond proactively to emerging threats while ensuring regulatory adherence and improving financial transparency. This research adopts a qualitative approach based on an extensive review of scholarly literature and existing enterprise security models to develop a comprehensive conceptual framework. The findings indicate that AI-driven platforms significantly improve operational efficiency, reduce security vulnerabilities, minimize compliance costs, and support strategic business intelligence. The study concludes that the convergence of AI, cybersecurity, financial analytics, and autonomous operations represents a sustainable solution for modern enterprises seeking secure, intelligent, and adaptive digital transformation