A Unified Logging and Monitoring Framework for Auditability of AI-Augmented Salesforce Workflows
Keywords:
Auditability, AI observability, Salesforce, CRM, unified logging, monitoring, workflow transparency, enterprise AI governanceAbstract
As artificial intelligence (AI) becomes increasingly integrated into enterprise Customer Relationship Management (CRM) systems like Salesforce, the need for robust logging and monitoring mechanisms grows in parallel. This paper proposes a unified framework for logging and monitoring that ensures auditability, transparency, and compliance in AI-augmented Salesforce workflows. By merging AI observability with traditional audit trails and real-time system diagnostics, the framework fosters responsible AI usage and addresses organizational demands for explainability and accountability. We analyze current literature and industry applications to construct a scalable and secure architecture tailored to dynamic CRM environments.
References
Amershi, Saleema, et al. “Guidelines for Human-AI Interaction.” Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 2019.
Veeravalli, S.D. (2023). Proactive Threat Detection in CRM: Applying Salesforce Einstein AI and Event Monitoring to Anomaly Detection and Fraud Prevention. ISCSITR-International Journal of Scientific Research in Artificial Intelligence and Machine Learning (ISCSITR-IJSRAIML), 4(1), 16–35. http://www.doi.org/10.63397/ISCSITR-IJSRAIML_04_01_002
Bates, Adam, Kevin R. B. Butler, and Patrick McDaniel. “Trustworthy Whole-System Provenance for the Cloud.” USENIX Security Symposium, 2016.
Brundage, Miles, et al. The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation. Future of Humanity Institute, University of Oxford, 2018.
Chandramouli, Raj, Michaela Iorga, and Santosh Chokhani. NIST Interagency Report 8228: Security Considerations for Cloud-Connected Devices. National Institute of Standards and Technology, 2019.
Dehghantanha, Ali, and Reza M. Parizi. Handbook of Big Data Privacy. Springer, 2020.
Veeravalli, S.D. (2023). Next-Generation APIs for CRM: A Study on GraphQL Implementation for Salesforce Data Integration. ISCSITR-International Journal of ERP and CRM (ISCSITR-IJEC), 4(1), 1–21. http://www.doi.org/10.63397/ISCSITR-IJEC_04_01_001
Kelley, Timothy, and Amy B. Randolph. “Salesforce Adoption and CRM Value: A Conceptual Framework and Empirical Validation.” International Journal of Information Management, vol. 45, 2019, pp. 175–184.
Lepri, Bruno, et al. “Fair, Transparent, and Accountable Algorithmic Decision-Making Processes.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 376, no. 2128, 2017.
Veeravalli, S.D. (2022). Beyond Roles and Profiles: A Hybrid Access Control Model for Granular Security in Salesforce CRM. International Journal of Computer Science and Engineering Research and Development (IJCSERD), 12(1), 95–111. http://www.doi.org/10.63519/IJCSERD_12_01_008
Mittelstadt, Brent D., et al. “The Ethics of Algorithms: Mapping the Debate.” Big Data & Society, vol. 3, no. 2, 2016.
Raji, Inioluwa Deborah, and Joy Buolamwini. “Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products.” Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019, pp. 429–435.
Wang, Zhen, Xinyu Hu, and Yanjun Liu. “Towards Unified Observability in Microservices Environments.” IEEE Transactions on Services Computing, vol. 15, no. 1, 2020, pp. 151–163.
Veeravalli, S.D. (2022). Legacy System Modernization: Guidelines for Migrating from Legacy Systems to Salesforce—Addressing Challenges and Implementing Best Practices with Reusable Integration Blueprints. International Journal of Computer Science and Information Technology Research (IJCSITR), 6(1), 133–144. https://doi.org/10.63530/IJCSITR_2022_03_01_14
Zaharia, Matei, et al. “Structured Streaming: A Declarative API for Real-Time Applications in Spark.” USENIX Annual Technical Conference, 2016.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Charles L. Scott (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
