Akamai's 12x number turns shadow AI from a headcount problem into a shortlist

A population that touches AI models twelve times more often than the bottom half of the workforce is, by construction, the easiest population to find ^[1]. Volume is what telemetry is good at. The awkward part is that the same volume makes them the people you can least afford to simply block, because Akamai's own framing has them wiring models into operations rather than drafting emails, and adding autonomous agents that run inside the business and outside its guardrails ^[12].

The identity split explains why an approved-vendor list cannot be the control. Microsoft Copilot M365 keeps 90.55% of interactions inside corporate identity systems, and Gemini Enterprise 98.15% ^[5]. Microsoft Copilot Standard is 63.92% personal logins ^[6]. Same vendor, same brand, opposite governance outcome, and the variable is the licence tier rather than the model. DeepSeek sits at 99.8% personal identity ^[6], which is a procurement fact disguised as a usage statistic.

Then there is an addition the report leaves to the reader. Personal identities account for 47.11% of enterprise AI conversations ^[4]. A further 14.4%, according to Akamai's Or Eshed, run through corporate email addresses attached to personal freemium subscriptions, where injected data may end up in public model training ^[7]. Those are described as distinct categories, so on the report's own numbers roughly 61.5% of enterprise AI conversation happens outside an enterprise-managed licence ^[16]. Half of that total will pass an identity check cleanly.

Extensions are where the concentration argument gets concrete. Midsize enterprises report 17.7% of employees running at least one AI extension against 9.53% at larger organisations, about 1.9 times the rate ^[8][17]. Nearly three-quarters request high or critical permissions ^[9], and 16.31% carry known CVEs against 10.80% for browser extensions generally, about half again as many ^[10][15]. Smaller estates, more installs, worse packages.

Two cautions on the load-bearing figure. The 12x is an interaction-rate ratio; the conversation-length gap is smaller, 18-plus prompts against a workforce average of about five, or roughly 3.6 times ^[3][14]. And every number here comes from one vendor's telemetry, published by Akamai, quoted by an executive who runs Akamai's enterprise security product and engineering ^[2][18]. The report counts exposure, not incidents.

What survives the caution is the shape. Of everything described, extensions are the only surface with a hard artefact behind them: an installed package, a permission grant, a CVE identifier. The identity leakage is a licensing question. The agents are behaviour. Eshed's own recommendation is to work out which employees depend most on AI in order to know where risk sits ^[13], and that is a smaller piece of work than governing everyone, which is presumably why the long tail of dozens of niche tools has stayed unmeasured while the frontier vendors got the policy documents ^[11].