Interaction-Aware Security

Interaction-aware security represents an approach to protecting AI systems that monitors and controls how different components, models, and users interact within a governance environment. This matters for enterprise compliance because modern AI systems rarely operate in isolation; they integrate with APIs, data pipelines, third-party services, and human decision-makers whose interactions can create new vulnerabilities and attack surfaces. Organizations adopting interaction-aware security can detect anomalous patterns in system behavior, enforce contextual access controls, and audit the full chain of exchanges that influence AI outputs and decisions.

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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