A Risk-Aware AI Governance Framework for Rural Clinics and Cloud Financial Systems: Cybersecurity Strengthening and Autonomous Threat Detection in DevOps Environments
DOI:
https://doi.org/10.15680/IJMRSETM.2025.0104001Keywords:
AI governance, risk-aware frameworks, cybersecurity, autonomous threat detection, DevOps, MLOps, rural healthcare, cloud financial systems, data protection, continuous monitoring, compliance, resilienceAbstract
This paper proposes a unified, risk-aware AI governance framework tailored for two critical yet contrasting domains—rural healthcare clinics and cloud-based financial systems. As both sectors increasingly adopt AI-driven workflows, they face heightened exposure to cyber threats, data integrity risks, and operational vulnerabilities. The framework integrates principles of responsible AI, domain-specific regulatory requirements, and continuous assurance practices into a DevOps and MLOps pipeline. It emphasizes adaptive risk scoring, explainability standards, privacy preservation, and compliance-aligned monitoring to safeguard patient and financial data. A multilayer cybersecurity model is outlined, incorporating autonomous threat detection, anomaly-aware observability, and automated incident-response orchestration. The proposed approach demonstrates how resource-constrained rural clinics and high-availability financial cloud systems can leverage shared governance controls while implementing context-appropriate hardening measures. By embedding risk-aware AI governance into the full software and model lifecycle, the framework aims to improve resilience, transparency, and trust across diverse operational environments.
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