A Secure Multi-Tenant AI Framework for Enterprise CRM Automation on Salesforce Cloud Platforms
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V6I2P113Keywords:
Multi-Tenant Architecture, Ai-Driven Crm, Salesforce Cloud, Enterprise Automation, Data Security, Explainable AIAbstract
As systems used in businesses to maintain and monitor customer data have become more popular, Customer Relationship Management (CRM) systems have transformed into sophisticated systems that facilitate the decision-making process, customer communications, and automated business processes. As cloud computing and Artificial Intelligence (AI) continue to gain popularity, the contemporary CRM systems are likely to provide real-time intelligence, personalization, and automation without causing any significant risks to the quality of data security, privacy, and regulatory compliance. This is especially tricky in multi-tenant cloud systems where a group Of Organizations Are Sharing Infrastructure But They Require Total Logical Data Segregation. This Project Suggests An Enterprise CRM Automation through this paper, which is a Secure Multi-Tenant AI Framework on the Salesforce Cloud Platform. The framework combines AI-help automation features, including predictive analytics, intelligent lead scoring, customer sentiment analysis, and workflow orchestration, into the standard multi-tenant framework of Salesforce. The data segregation, access control, encryption, and AI governance mechanisms are given special attention to make sure the data is confidential and trustworthy among tenants. The suggested framework exploits Salesforce native services, such as metadata-driven settings, role-based access control (RBAC), and abstraction layers of AI models, as well as secure API gateways. Tenant-aware pipelines are used to deploy AI services to avoid the leakage of data and contamination of the models. Explainable AI (XAI) elements are also included in the framework to increase transparency and regulatory compliance. The comprehensive approach is provided, including the architectural design, security implementation, AI life cycle management, and automation processes. It is evaluated experimentally with datasets of enterprise CRM against simulated tenants, with respect to the efficiency of automation, security compliance, and scalability of the system. Findings reveal high accuracy of process automation, customer responsiveness, and efficiency of operations, and the high isolation guarantees. The study serves as a viable and scalable and secure roadmap to the implementation of AI-driven CRM automation in enterprise cloud-based systems and provides an insight that applies in organizations that implement intelligent CRM solutions developed on Salesforce.
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