Rethinking AI Security: Why CASB and DLP Need an Interaction-Aware Layer

Build your strategy around answering these questions to ensure employees use AI productively while keeping sensitive data, IP, and agent behavior within the boundaries set for safe AI use. The post Rethinking AI Security: Why CASB and DLP Need an Interaction-Aware Layer appeared first on SecurityWeek .

The article explains that traditional CASB and DLP controls govern cloud app access and detect sensitive data patterns, but they cannot see inside AI prompts, responses, or agent instructions, creating a material control gap for AI usage in enterprises.[1][2][3][5] It argues for an ‘interaction-aware’ inspection layer that evaluates prompt semantics, response sensitivity, and whether agent actions are authorized, treating prompt injection and agent misuse as everyday operational risks rather than edge cases.[1][2][5] From a RealGround standpoint, this highlights AI agent abuse and indirect prompt injection risks within AI workflows, and the need to extend security from file-centric and network-centric controls to runtime interaction-level monitoring, least-privilege agent permissions, and anomaly detection on AI behavior.[2][5] Practically, organizations should audit CASB/DLP gaps for AI interactions, define policies for allowed AI use, and implement gateway-level controls that classify and constrain agent actions based on intent and data sensitivity.