Autonomous AI Cloud Security and Risk Governance for Enterprise Digital Ecosystems and Financial Networks
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Abstract
The expansion of cloud-based enterprise digital ecosystems and financial networks has created a pressing need for autonomous security and risk governance solutions. Traditional security measures often fail to address the speed, complexity, and sophistication of modern cyber threats, especially in highly distributed and multi-tenant cloud environments. This study proposes an autonomous AI-driven cloud security and risk governance framework designed to protect enterprise digital ecosystems and financial networks through real-time threat detection, adaptive risk management, and intelligent compliance monitoring. Leveraging machine learning, deep learning, and behavioral analytics, the framework continuously analyzes network activity, transaction data, and cloud workloads to identify anomalies and predict emerging threats. Integrated cloud-native technologies, including containerized microservices and orchestration platforms, provide scalability, resilience, and operational continuity. The framework incorporates zero-trust access models, automated policy enforcement, and intelligent risk governance to ensure compliance with financial regulations, mitigate insider threats, and maintain data integrity. Autonomous decision-making mechanisms enable proactive threat mitigation, reducing operational disruption and minimizing human intervention. By combining AI-driven analytics, cloud-native scalability, and risk governance, this research demonstrates a comprehensive approach to securing enterprise and financial systems, providing organizations with adaptive, resilient, and regulatory-compliant cloud security capabilities in an increasingly hostile cyber environment.
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