Data Security Considerations For Building Enterprise AI Agents

InnovationData Security Considerations For Building Enterprise AI AgentsByQuang Tuan Dang,Forbes Councils Member.for Forbes Technology CouncilCOUNCIL POSTExpertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based)May 11, 2026, 09:45am EDTTony Dang is co-founder & CTO at Infisical, an identity and security infrastructure platform. gettyAI agents and custom AI-powered applications are rapidly becoming commonplace in production. But to implement them, engineering teams are connecting large language models (LLMs) to internal databases, customer records, proprietary codebases and operational systems.This, of course, expands the data security surface. Each time an enterprise sends a query to an LLM provider, it starts a data pipeline that flows sensitive information outside organizational boundaries. And every time an agent acts on untrusted input, it creates an opportunity for that pipeline to be exploited.As CTO of Infisical, I spend a lot of time thinking about security infrastructure. In this article, I explore the data security risks that enterprises should be thinking about when building custom AI applications and agents, and the practical controls that can reduce exposure today.​Building Classification And Redaction Into The Data PipelineWhen an enterprise builds a custom AI application, whether it's a support agent, a code review tool or an internal knowledge assistant, it typically connects to an LLM provider via API. Any data you want the model to reason over must be sent to the provider.This means that if your agent summarizes customer support tickets, the contents of those tickets leave your infrastructure. If it searches internal documentation to answer employee questions, the relevant documents are included in the request payload.Organizations need to internalize a simple principle: Calling an LLM API is a data transfer. You're trusting the provider with every piece of inf...