Article contents
Privacy-Preserving Computing: Managing Sensitive Data in Salesforce Enterprise Systems
Abstract
The digital transformation of contemporary business operations has fundamentally transformed how organizations collect, process, and store customer data within enterprise Customer Relationship Management platforms. Salesforce environments have emerged as central repositories for sensitive information, creating unprecedented imperatives for robust privacy protection frameworks that address evolving regulatory landscapes, including GDPR, HIPAA, and CCPA requirements. Privacy-preserving computing represents a critical paradigm that enables organizations to leverage data insights while maintaining individual privacy and regulatory compliance through comprehensive technical and procedural methodologies. Multi-layered security approaches have become essential for protecting sensitive data within Salesforce environments, as traditional single-point security measures prove insufficient against sophisticated cyber threats. The article examines essential privacy-preserving techniques, including data masking, encryption technologies, tokenization, and differential privacy that collectively address diverse organizational requirements and regulatory contexts. Implementation strategies require systematic planning that integrates technical capabilities with governance frameworks, emphasizing the importance of data discovery, classification initiatives, and development lifecycle integration. Privacy protection has evolved from a regulatory compliance obligation into a fundamental business imperative that directly enhances organizational reputation and competitive positioning. Organizations implementing comprehensive privacy frameworks experience enhanced stakeholder confidence, improved regulatory relationships, and strengthened market differentiation. The convergence of technological capability and regulatory necessity creates environments where privacy protection becomes a strategic differentiator rather than an operational burden.
Article information
Journal
Journal of Computer Science and Technology Studies
Volume (Issue)
7 (9)
Pages
433-438
Published
Copyright
Open access

This work is licensed under a Creative Commons Attribution 4.0 International License.