AI-Driven Software Ecosystem Engineering with Oracle E-Business Suite: Interpretable Machine Learning for Cloud-Based Data Security and Firewall Rule Optimization
DOI:
https://doi.org/10.15680/IJCTECE.2023.0605006Keywords:
Oracle E-Business Suite, interpretable machine learning, cloud security, firewall rule optimization, database security, MLOps, explainable AI, role-based access control, anomaly detection, complianceAbstract
Enterprises running Oracle E-Business Suite (EBS) increasingly migrate components and integrations to cloud environments, creating new opportunities — and new risks — for data security, configuration drift, and network-level exposure. This paper presents an AI-driven software-ecosystem engineering approach that integrates interpretable machine learning for (1) anomaly detection in access patterns to EBS modules and databases, (2) automated firewall rule optimization to reduce attack surface while preserving business connectivity, and (3) cloud-native data security patterns (encrypted indices, fine-grained RBAC audits, and policy-as-code enforcement). The proposed ecosystem couples EBS telemetry (audit logs, JDBC/OCI connection traces, application logs) with cloud network metadata and identity/access events to build a feature store that powers lightweight, explainable models (decision trees, rule lists, SHAP-annotated ensembles) suitable for security operations (SecOps) and application teams. A rule-synthesis engine translates model outputs into candidate firewall changes (allow/deny refinements, port consolidations, and zone tightening) and ranks them by estimated business impact and risk reduction, while automated safety checks ensure no disruption to production flows.
We describe an engineering blueprint for integrating these capabilities into an Oracle EBS estate: non-invasive telemetry collectors, a model development lifecycle emphasizing interpretability and human validation, canary deployments for firewall rule proposals, and automated rollback/playbooks tied to observability. Evaluation uses a mixed methodology: retrospective attack/simulation replay on de-identified EBS logs, live shadow deployments in staging environments, and a limited production pilot with human-in-the-loop approval. Expected outcomes include measurable reduction in overly permissive rules, earlier detection of anomalous EBS access patterns, and maintenance of application availability. We discuss trade-offs: false positives from aggressive rule tightening, the need for continuous model governance to manage drift, and the operational cost of telemetry and rule verification. The contribution is a practical, interpretable AI pipeline for securing Oracle EBS in cloud settings that aligns SecOps automation with application availability and compliance requirements.
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