Zero Data Retention in LLM-based Enterprise AI Assistants: A Comparative Study of Market Leading Agentic AI Products

• URL: http://arxiv.org/abs/2510.11558v1
• Date: Mon, 13 Oct 2025 16:00:34 GMT
• Title: Zero Data Retention in LLM-based Enterprise AI Assistants: A Comparative Study of Market Leading Agentic AI Products
• Authors: Komal Gupta, Aditya Shrivastava,
• Abstract summary: Governance of data, compliance, and business privacy matters, particularly for healthcare and finance businesses.<n>Recent emergence of AI enterprise AI assistants enhancing business productivity, safeguarding private data and compliance is now a priority.<n>With the implementation of AI assistants across the enterprise, the zero data retention can be achieved by implementing zero data retention policies.
• Score: 0.12277343096128711
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Governance of data, compliance, and business privacy matters, particularly for healthcare and finance businesses. Since the recent emergence of AI enterprise AI assistants enhancing business productivity, safeguarding private data and compliance is now a priority. With the implementation of AI assistants across the enterprise, the zero data retention can be achieved by implementing zero data retention policies by Large Language Model businesses like Open AI and Anthropic and Meta. In this work, we explore zero data retention policies for the Enterprise apps of large language models (LLMs). Our key contribution is defining the architectural, compliance, and usability trade-offs of such systems in parallel. In this research work, we examine the development of commercial AI assistants with two industry leaders and market titans in this arena - Salesforce and Microsoft. Both of these companies used distinct technical architecture to support zero data retention policies. Salesforce AgentForce and Microsoft Copilot are among the leading AI assistants providing much-needed push to business productivity in customer care. The purpose of this paper is to analyze the technical architecture and deployment of zero data retention policy by consuming applications as well as big language models service providers like Open Ai, Anthropic, and Meta.

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