Secure ML Model Deployment Using Oracle OCI for ERP/EPM: A Secure and Scalable Enterprise AI Framework
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V4I1P114Keywords:
Secure ML Deployment, Oracle OCI, ERP, EPM, Enterprise AI, Cloud Security, Model Governance, AI Scalability, Data Privacy, Enterprise AnalyticsAbstract
A high pace of use of artificial intelligence (AI) and machine learning (ML) techniques in enterprise resource planning (ERP) and enterprise performance management (EPM) systems has revolutionized the decision-making, automation and operational performance in organizations. Nevertheless, the practical implementation of ML models in enterprise-scale and securely is very diverse because of such topics as the confidentiality of data, compliance with regulations, model integrity and performance of the system. In this paper, the authors provide a secure and scalable infrastructure of deploying ML models in ERP/EPM systems based on Oracle Cloud Infrastructure (OCI). The suggested structure will utilize the OCI security capabilities, container orchestration, identity and access control, and high-performance computing to offer a powerful platform on which AI can be deployed. The benefits of this work are the following: (i) highly detailed architecture of secure ML deployment in ERP/EPM, (ii) applying the best practices of model security and data governance, (iii) performance analysis of ML inference on OCI with enterprise data, and (iv) the comparison with the traditional on-premises deployment strategies. The experimental findings show the proposed framework promotes a high level of security, scalability, and efficient operations and complies with the enterprise data policies. The paper offers a reference architecture to organizations that need to implement AI-related analytics into ERP/EPM systems safely and resourcefully.
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