Workers Use Personal Accounts for Nearly Half of Enterprise AI Chats
Generative AI has spread rapidly through corporate workflows, but employees who use unapproved tools or accounts create a layer of “shadow AI” outside company oversight. The practice can expose customer information, internal documents or source code to third-party services, while weak identity controls make access difficult to audit. Data entered through consumer accounts may also be retained or used to improve publicly available models, raising security, privacy and compliance concerns.
Akamai said its latest research found that nearly half of employees’ enterprise AI conversations were conducted through personal accounts, leaving a substantial share of activity beyond corporate identity and permission systems. Security specialists recommend that companies adopt identity federation measures such as single sign-on, or SSO, to centralize access policies, manage account lifecycles and preserve usage records. The findings underscore a widening governance gap as workplace AI adoption outpaces formal controls.
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The history behind this eventShadow AI Spreads Through Workplaces, Prompting Stronger Corporate Governance
“Shadow AI” refers to employees entering work-related data into external generative AI tools without authorization. While such tools can make writing, analysis and software development more efficient, they may put customer data, source code and trade secrets beyond corporate control. They can also create copyright, privacy and compliance liabilities, making the practice a key focus of corporate cybersecurity governance.
Recent reports said shadow AI had spread through workplaces, exposing companies to the risk of confidential information leaks as employees privately use such tools. Experts recommend that cybersecurity, legal and IT teams establish approved-tool lists, data-classification rules, input restrictions and audit procedures, while also deploying technical filtering controls. As of July 20, 2026, the provided material did not identify any specific organization, amount or incident date.
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