Secure AI Enabled Enterprise Ecosystems for Fraud Prevention Compliance Automation and Real Time Analytics
DOI:
https://doi.org/10.15680/vhvpvg58Keywords:
Secure AI, Enterprise Ecosystems, Fraud Prevention, Compliance Automation, Real-Time Analytics, AI Governance, Data Security, Cybersecurity Architecture, Regulatory Technology (RegTech), Risk Management, Machine Learning Security, Enterprise IntelligenceAbstract
The rapid digitalization of enterprises has led to increasingly complex data ecosystems where vast amounts of sensitive information are processed across distributed platforms. While artificial intelligence (AI) enhances operational efficiency, fraud detection, and analytics capabilities, it simultaneously introduces new security, governance, and compliance challenges. This paper explores the design and implementation of secure AI-enabled enterprise ecosystems that integrate fraud prevention, automated regulatory compliance, and real-time analytics within a unified architectural framework. The proposed model emphasizes secure data pipelines, AI-driven anomaly detection, automated policy enforcement, encryption mechanisms, identity-centric access control, and continuous monitoring. By embedding security controls directly into AI workflows and analytics infrastructures, enterprises can proactively detect fraud, ensure compliance with evolving regulations, and generate actionable insights in real time. The study outlines architectural components, governance models, AI integration strategies, and system validation approaches. A comprehensive research methodology is proposed to evaluate performance, security resilience, and compliance effectiveness. While the integration of AI and security enhances enterprise intelligence and risk mitigation, implementation complexity, resource demands, and model vulnerabilities remain challenges. The paper concludes by identifying benefits, limitations, and future research directions for secure AI-enabled enterprise ecosystems.
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