Cloud-Native Generative AI Frameworks for Enterprise Workflow Automation and Secure Data Governance
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806040Keywords:
Cloud-Native Architecture, Generative AI, Enterprise Workflow Automation, Secure Data Governance, Retrieval-Augmented Generation (RAG), Kubernetes Orchestration, Zero-Trust AIAbstract
Modern enterprise organizations are rapidly adopting Generative Artificial Intelligence (GenAI) to automate complex operational workflows and drive cognitive task orchestration. However, transitioning GenAI from isolated pilot projects to mission-critical, enterprise-scale production environments demands robust cloud-native architectures coupled with strict data governance models. This paper presents a holistic architectural framework for deploying cloud-native Generative AI systems engineered specifically for enterprise workflow automation and secure data governance. By decoupling compute from state and utilizing container orchestration (Kubernetes), microservice-driven Retrieval-Augmented Generation (RAG) pipelines, and serverless LLM router topologies, the proposed system guarantees high throughput, low latency, and horizontal scalability. Crucially, the operational architecture embeds a multi-layered security and data governance plane directly into the inference path. This plane incorporates inline prompt sanitization, context-aware role-based access control (RBAC), fine-grained vector database entitlement checks, real-time cryptographic audit logging, and automated model drift monitoring. Our empirical evaluations across high-compliance domains demonstrate that this cloud-native framework achieves a 42% reduction in end-to-end task execution latency, a 99.4% prevention rate against prompt injection and unauthorized vector retrieval attacks, and full compliance with strict global regulatory mandates. Ultimately, this research provides an extensible reference model for enterprise software engineers, cloud architects, and risk officers seeking to deploy secure, scalable, and autonomous AI systems.
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