DLP

Data Loss Prevention (DLP) refers to security technologies and practices that detect and prevent unauthorized transmission or exfiltration of sensitive data outside an organization's network. DLP systems monitor data in motion, at rest, and in use across endpoints, networks, and cloud applications to enforce policies and block violations before they occur. For AI governance specifically, DLP is critical because large language models and generative AI systems can inadvertently leak proprietary information, personal data, or trade secrets during training, fine-tuning, or inference if not properly controlled.

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ResearchGlobal2026-08-04

CASB and DLP Cannot See Inside AI Prompts. That Is Now a Material Control Gap.

A SecurityWeek analysis co-authored with Cato Networks argues that traditional cloud access security broker and data loss prevention tools are structurally unable to detect AI-specific risks because those risks occur inside prompt content and model responses, not at the application access layer. The piece calls on enterprise security and governance teams to add an interaction-aware inspection layer covering prompt intent, response sensitivity, and agent action authorization. Agentic workflows are identified as the highest-urgency area, with prompt injection elevated from an edge case to a core operational risk.

CASB DLP agentic AI prompt injection Cato Networks data governance interaction-aware security