Securing Enterprise SaaS AI Agents in Financial Services

SaaS AI Agents, such as Microsoft Copilot and Google Gemini Enterprise, are unlike typical chatbots, which have limited access to data. On the contrary, these are large-scale deployments that are tied to core live systems with extensive access to enterprise-wide data.

For most BFSI enterprises, these agents are released into the wild when a vendor auto-configures a module or a business unit enables a feature. With no appropriate labeling applied to sensitive, regulated data that tells the AI agent what data to surface and what to leave alone, and with the years of access sprawl the AI agent inherits, enterprise data estates are left exposed to risks such as sensitive data exposure, regulatory compliance violations, and security breaches.

This whitepaper takes a deep dive into the layered approach to closing that gap by discovering sensitive data that lives across SharePoint, Google Drive, and other SaaS environments, governing ROT data that AI agents could access without appropriate classification and labeling, governing user and machine identities to curb access sprawl, and surgically reversing AI Agents’ mistakes without rolling back the entire systems.

Download the whitepaper and learn how to operationalize that layered, AI security and governance roadmap to enable safe deployments of enterprise SaaS AI Agents.