Secure AI Orchestration for Construction Collaboration Platforms

Abstract: AI agents are being used in construction collaboration platforms increasingly to perform tasks related to planning, safety, subcontracting, document validation, and inspection among others. However, highly sensitive data such as contractual details, finances, personnel information, and design layouts are processed through collaboration platforms. In order to facilitate coordinated functions among different AI agents without compromising their integrity, security, and regulatory compliance, this paper proposes a Secure AI Orchestration Framework (SAIOF) that would support collaboration platforms development. The SAIOF framework encompasses role-based access controls, multi-agent coordination through policies, agent orchestration federations, and encrypted communication among AI agents. The proposed model will allow inspectors, subcontractors, and project managers to collaborate securely without unnecessarily exposing sensitive or confidential data. As can be seen from the simulation experiment of a construction collaboration workflow, there is less risk of data leaks, greater efficiency, and greater workflow transparency with this orchestration approach. From the analysis of experimental results, it follows that secure orchestration improves the accuracy of coordination actions, limits malicious attempts to gain unauthorized access to sensitive information, and increases scalability in the deployment of AI systems in smart buildings.

Keywords: AI orchestration, intelligent coordination, safe multi-agent systems, federated AI, construction cybersecurity, access control, and privacy-preserving AI