With AI spending forecasted to hit $2.5 trillion in 2026, and with 40% of enterprise apps expected to embed task-specific AI agents by the end of 2026, the real question is no longer about adoption, it’s about visibility and control. With numbers like these, it is clear that AI integration is scaling quickly, but there is a security gap.
While AI security checks are catching up quickly, rising from 37% in 2025 to 64% in 2026, that still leaves over a third without a formal assessment. This is why the right permissioning often lags behind.
As I have observed, when agents operate across multiple tools and systems, organizations are no longer managing just “AI output quality.” They’re managing action pathways, often in environments where it’s difficult to pinpoint where a request went wrong, where an input was manipulated, or which step triggered the final action. Permissioning, in this context, becomes the difference between useful automation and unauthorized behavior at scale.
