Governance-Aware Secure Architecture for Real-Time Enterprise Data Exchange across Financial Healthcare and Advertising Platforms Using Generative AI
DOI:
https://doi.org/10.15680/ekhtqc25Keywords:
Generative AI, enterprise data exchange, governance-aware architecture, cybersecurity, healthcare data security, financial systems, digital advertising, zero-trust architecture, privacy-preserving analytics, cloud computingAbstract
The rapid digital transformation of enterprises has intensified the need for secure, interoperable, and governance-aware data exchange across heterogeneous domains such as financial services, healthcare systems, and digital advertising platforms. These sectors manage highly sensitive information, including personal health records, financial transactions, and behavioral analytics, making them prime targets for cyber threats, data misuse, and regulatory non-compliance. Traditional data exchange frameworks often lack real-time governance enforcement, cross-domain trust mechanisms, and intelligent automation required to support modern enterprise ecosystems. This paper proposes a governance-aware secure architecture that integrates Generative AI with cloud-native microservices, zero-trust security models, and policy-driven data exchange protocols to enable real-time and compliant enterprise communication across financial, healthcare, and advertising environments.
The proposed architecture introduces a multi-layered framework comprising secure data ingestion pipelines, AI-driven governance engines, federated identity management, privacy-preserving computation, and intelligent monitoring systems. Generative AI models are utilized for adaptive policy enforcement, anomaly detection, automated compliance reporting, and contextual data transformation. The architecture supports interoperability across enterprise resource planning (ERP), healthcare information systems, and programmatic advertising platforms while ensuring adherence to regulatory standards such as GDPR, HIPAA-like policies, and financial compliance requirements.
Results indicate that integrating Generative AI within governance-aware architectures significantly enhances threat detection accuracy, reduces policy violations, and improves cross-domain data exchange efficiency. The framework also demonstrates improved scalability, reduced latency in secure data transactions, and enhanced transparency through audit-ready logs and explainable AI governance mechanisms. By combining real-time analytics, zero-trust principles, and AI-driven compliance automation, the proposed architecture provides a unified and resilient solution for secure enterprise data ecosystems.
This research contributes a novel cross-domain architecture that bridges governance, security, and AI-enabled automation for modern enterprise environments. The findings highlight the potential of Generative AI to transform enterprise data governance by enabling adaptive, intelligent, and secure real-time data exchange across multiple regulated sectors.
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