Threat Detection and Data Breach Analysis in Salesforce CRM Environments: The LTDF Machine Learning Framework
Author(s): Lalith Chandra Bandaru
Publication #: 2605034
Date of Publication: 11.06.2021
Country: USA
Pages: 1-12
Published In: Volume 7 Issue 3 June-2021
DOI: https://doi.org/10.62970/IJIRCT.v7.i3.2605034
Abstract
Enterprise Salesforce CRM deployments sit on some of the most commercially sensitive data organisations hold, yet the detection tooling applied to them typically lags several generations behind what protects on-premises infrastructure. The threat surface is unusual: there is no network perimeter to defend, and malicious activity — OAuth credential abuse, permission escalation, insider data harvesting — arrives over the same authenticated HTTPS endpoints as entirely legitimate work. We developed LTDF (Layered Threat Detection Framework), a machine learning ensemble that combines an LSTM sequence classifier, an Isolation Forest anomaly detector, and a CVSS-weighted risk scoring engine to identify security incidents in Salesforce environments in near real time. The framework extracts twenty-eight behavioural features from Salesforce Platform Events, Event Monitoring logs, and Login History over five-minute sliding windows. Across eleven production Salesforce organisations over eighteen months covering 847 confirmed incidents, LTDF achieves a true positive rate of 94.8% with a false positive rate of 2.8%, cutting mean time to detect from 24 minutes to 4.7 minutes and mean time to contain from 60 minutes to 9.1 minutes. The system integrates with existing SIEM platforms and supports automated response — OAuth token revocation, session termination, IP restriction — without requiring any changes to the underlying Salesforce org configuration.
Keywords: Salesforce security, CRM threat detection, LSTM, anomaly detection, insider threat, OAuth abuse, machine learning, SIEM integration, behavioural analytics, data breach analysis.
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