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Secure and Scalable CRM: Hybrid Access Control, Event Monitoring, and AI-Driven Fraud Detection in Salesforce

Dr. Veeranna Kotagi

Abstract


The rapid digitalization of customer engagement has made customer relationship management (CRM) systems essential to enterprise strategy. Yet, as CRM platforms scale to integrate diverse data sources, IoT streams, and AI-driven engagement, they face heightened risks of fraud, insider threats, and compliance challenges. Salesforce, as the global leader in CRM, has pioneered an approach to secure and scalable CRM that combines hybrid access control, real-time event monitoring, and AI-driven fraud detection. This paper analyzes Salesforce’s architecture for balancing security and scalability, emphasizing the role of hybrid access models, asynchronous tools, and predictive analytics in building resilient platforms.

Drawing on Veeravalli’s extensive work (2022–2025) and other scholarly contributions, the study finds that Salesforce embeds security within its architecture while scaling to meet enterprise demands. Hybrid access control models provide granular, context-aware permissions, event monitoring ensures behavioral visibility, and Einstein AI operationalizes anomaly detection for fraud prevention. The analysis concludes that Salesforce represents a model for trustworthy, scalable, and intelligent CRM ecosystems. Future research should explore zero-trust CRM, LLM-powered anomaly detection, and autonomous compliance frameworks.


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References


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