Responsible AI-Driven Cloud and Software-Defined Network Architecture for Ethical Automation of Business Rules in Oracle-Based BMS Systems
DOI:
https://doi.org/10.15662/IJRAI.2022.0506013Keywords:
AI automation, ethical AI, business-rule management, cloud computing, software-defined networking, real-time automation, governance, transparency, fairness, rule-engineAbstract
In modern enterprise ecosystems, the convergence of cloud computing, software-defined networking (SDN) and business-rule automation offers transformative potential—but also significant ethical challenges. This paper presents an AI-Driven Ethical Automation Architecture designed for real-time business rule management in cloud-based SDN environments. The architecture integrates a business-rule engine with an AI decision module, an SDN control and orchestration layer, and a governance subsystem that embeds ethical oversight—ensuring transparency, accountability, fairness, and privacy in automated rule enforcement. The business-rule engine receives rule definitions from business stakeholders, the AI module monitors network and cloud state in real time, predicts rule-conflicts or violations, prioritises enforcement actions, and triggers rule application via SDN flows. Concurrently, the governance subsystem logs decisions, produces explainable rationales, detects bias in rule outcomes (e.g., discrimination among clients), and supports rollback or human-override. We describe the architecture, its components and data/control flows, then present a simulation/prototype evaluation in a cloud-SDN environment under dynamic conditions (changing loads, rule changes, network faults). Key metrics include rule enforcement latency, throughput of rule-activated flows, number of rule-conflicts detected/automated resolved, fairness index across business classes, and governance overhead (latency, logging cost). Results show that our architecture reduces rule-enforcement latency by ~30 %, automates ~70 % of conflict resolution, improves fairness index by ~20 % compared to a baseline without AI/governance, while introducing a modest overhead of ~8 % additional latency due to logging/explanation. We discuss the trade-offs between agility, automation and ethics/governance, and outline deployment considerations. The contribution lies in bridging business-rule automation, SDN/cloud orchestration and ethical AI governance in a unified architecture for real-time dynamic environments. Future work includes extending to multi-tenant federated clouds, richer rule languages, continuous ethics-monitoring loops and human-in-the-loop hybrid automation.
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