AI-Driven Secure Cloud Workspaces for Strengthening Coordination and Safety Compliance in Distributed Project Teams
DOI:
https://doi.org/10.15662/IJRAI.2022.0506017Keywords:
AI-enhanced collaboration, cloud workspace, distributed teams, team coordination, remote project management, intelligent assistant, meeting summarization, coordination engine, safetyAbstract
The rapid expansion of geographically distributed project teams has intensified the need for intelligent, secure, and collaborative digital environments. This paper presents an AI-driven cloud workspace framework designed to enhance team coordination, streamline communication, and strengthen safety compliance across distributed project environments. The proposed system integrates real-time analytics, intelligent task orchestration, automated safety monitoring, and context-aware alerts to improve operational visibility and decision-making. Machine learning models evaluate project workflows, detect deviations, predict bottlenecks, and recommend proactive safety measures. Cloud-native security mechanisms, including role-based access control, encrypted data channels, and continuous threat monitoring, ensure robust protection for sensitive project information. Additionally, the framework supports multi-device accessibility and cross-platform integrations to enable seamless collaboration across diverse locations and time zones. Experimental evaluation demonstrates notable improvements in task alignment, response time, risk mitigation, and compliance adherence. Overall, the AI-driven secure cloud workspace significantly elevates team coordination and safety performance, offering a scalable solution for modern distributed project teams.
This research contributes to theory and practice by demonstrating how intelligent, AI-powered workspaces can augment coordination in distributed settings. It offers design principles for embedding AI into collaborative platforms and highlights risks such as over-notification, privacy concerns, and reliance on AI suggestions. Future work will explore scaling to large, multi-team programs, integrating richer behavioral models, and refining the AI’s interpretability to foster user trust.
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