Context Aware Secure and Governed AI Systems for Enterprise Analytics and Workforce Decisions and Digital Commerce
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Abstract
Enterprises increasingly rely on artificial intelligence to support analytics-driven decision-making across workforce management and digital commerce environments. However, the deployment of AI systems without sufficient contextual awareness, security controls, and governance mechanisms introduces risks related to bias, privacy violations, and operational failures. This study proposes a context aware secure and governed AI systems framework designed to enhance enterprise analytics while supporting responsible workforce decision-making and scalable digital commerce operations. The framework integrates contextual data modeling, advanced analytics, and AI-driven automation with embedded security, fairness, and governance controls. By incorporating enterprise data, behavioral signals, and external contextual factors, the proposed system enables adaptive decision intelligence that aligns with organizational objectives and regulatory requirements. The framework emphasizes explainability, auditability, and human oversight to ensure trustworthy AI adoption. This research contributes a unified architectural and methodological approach for deploying AI systems that balance innovation with accountability. The proposed model supports predictive insights, real-time personalization, and risk-aware decisions across enterprise domains, offering a scalable foundation for next-generation intelligent enterprises operating in complex digital ecosystems.
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