AI-Enabled Financial Data Governance Using a Secure Cloud-Based Framework for Enterprise Compliance and Automation
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Abstract
The rapid growth of financial data across enterprises has intensified the need for robust governance frameworks that ensure security, compliance, and operational efficiency. This paper proposes an AI-enabled financial data governance model integrated within a secure cloud-based infrastructure to address challenges such as regulatory compliance, data integrity, risk management, and automation. By leveraging artificial intelligence techniques—including machine learning, natural language processing, and anomaly detection—the framework enhances data classification, monitoring, and policy enforcement in real time. Cloud computing provides scalability, flexibility, and cost efficiency, enabling organizations to manage vast volumes of financial data while maintaining stringent security protocols.
The proposed framework aligns with global compliance standards and regulatory requirements by automating auditing processes, detecting irregularities, and ensuring transparency in financial reporting. Additionally, it reduces manual intervention, thereby minimizing human error and improving decision-making accuracy. This research highlights how integrating AI with cloud-based governance systems can transform enterprise financial data management into a proactive, intelligent, and adaptive process. The study also evaluates the benefits and limitations of the approach, offering insights into future advancements and adoption challenges. Ultimately, this model supports enterprises in achieving secure, compliant, and efficient financial data governance in an increasingly data-driven environment.
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