Forty percent of organizations have purchased enterprise LLM subscriptions while over ninety percent of employees actively use AI tools daily. Forty-five point four percent of sensitive AI interactions originate from personal email accounts, bypassing corporate controls. Employees largely drive AI adoption from the bottom up, frequently using unsanctioned or newer tools that improve productivity. Governance frameworks remain top-down and lag actual usage. Blocking known platforms fails because AI is embedded across SaaS apps, pushing employees to alternative tools, personal accounts, or home devices. Security and AI governance teams must discover, understand, and govern real employee AI activity to mitigate exposure.
In most cases, employees are driving adoption from the bottom up, often without oversight, while governance frameworks are still being defined from the top down. Even if they have enterprise-sanctioned tools, they are often eschewing these in favor of other newer tools that are better-placed to improve their productivity. Unless security leaders understand this reality, uncover and govern this activity, they are exposing the business to significant risks.
This approach seeks to restrict access to well-known AI platforms and hope adoption slows. The reality is different. AI is no longer a category that can be easily fenced off. From productivity apps like Canva and Grammarly to collaboration tools with embedded assistants, AI is woven into nearly every SaaS app. Blocking one tool only drives employees to another, often through personal accounts or home devices, leaving the enterprise blind to real usage.
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