Securing Salesforce Ecosystems: Cloud-Native Threat Detection and Automated Defenses in Enterprise Development
DOI:
https://doi.org/10.22399/ijcesen.4203Keywords:
Salesforce security, cloud-native threat detection, automated defenses, AI-driven detection, enterprise cybersecurity, complianceAbstract
The rapid adoption of Salesforce as a cloud-based enterprise platform has expanded opportunities for digital transformation while simultaneously increasing exposure to sophisticated cyber threats. This study investigates the effectiveness of cloud-native threat detection and automated defense mechanisms in securing Salesforce ecosystems across diverse industries. Using a mixed-methods approach that combined system log analysis, controlled attack simulations, and surveys from 210 Salesforce professionals, the research evaluated detection accuracy, false positive and false negative rates, incident response times, downtime, compliance scores, and industry-specific variations. Results revealed that AI-driven detection achieved the highest performance, with superior accuracy, precision, and recall compared to rule-based and anomaly-based methods. Fully automated defenses significantly reduced response times and downtime while maximizing containment success and data protection. Moreover, multi-factor authentication mitigated risks associated with integration complexity, and compliance adherence strongly correlated with higher defense effectiveness. The findings contribute to the limited body of Salesforce-specific security research and provide actionable insights for enterprises seeking to safeguard sensitive data and workflows in increasingly interconnected cloud environments.
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