Gartner Warns of Shadow AI Data Leaks

Gartner warned in a recent report that companies should respond quickly when data is exposed through “Shadow AI,” the use of AI services through personal accounts without company approval. Even when employees access ChatGPT on a company computer, personal accounts may fall outside corporate security policies, so using a work device alone does not protect confidential information entered into the service. Deleting a conversation from the screen is also separate from deleting the data from the service’s servers.

According to IBM’s 2026 Cost of a Data Breach report, Shadow AI was involved in 43% of all data breaches between March 2025 and February 2026. That was more than twice the share from the previous year, and the average cost of a related breach was $5.39 million per incident.

Even after a conversation is deleted, information may remain in server request and response logs, safety review systems, temporary storage, and backup servers. In general examples Gartner cited, deleted conversation records and server logs may be kept for about 30 days, data flagged for safety review for 2 to 7 years, and data collected for AI training for up to 5 years. Actual retention periods vary by service, account type, and data handling policy.

Information already incorporated into model training may continue to have an effect even after the source file is deleted, because input data is statistically reflected in many model parameters. Gartner said Machine Unlearning, a technique for removing learned information, also has practical limits. If a leak is confirmed, Gartner recommends checking AI training settings, blocking further inputs, changing exposed passwords and authentication details, asking the service provider to delete the data, and confirming whether server logs and backups are retained or deleted.

Gartner said companies should go beyond simply banning AI use. They should identify which services employees use and what information they enter, then establish approved services and data management standards.

Gartner warned in a recent report that companies should respond quickly when data is exposed through “Shadow AI,” the use of AI services through personal accounts without company approval. Even when employees access ChatGPT on a company computer, personal accounts may fall outside corporate security policies, so using a work device alone does not protect confidential information entered into the service. Deleting a conversation from the screen is also separate from deleting the data from the service’s servers.

According to IBM’s 2026 Cost of a Data Breach report, Shadow AI was involved in 43% of all data breaches between March 2025 and February 2026. That was more than twice the share from the previous year, and the average cost of a related breach was $5.39 million per incident.

Even after a conversation is deleted, information may remain in server request and response logs, safety review systems, temporary storage, and backup servers. In general examples Gartner cited, deleted conversation records and server logs may be kept for about 30 days, data flagged for safety review for 2 to 7 years, and data collected for AI training for up to 5 years. Actual retention periods vary by service, account type, and data handling policy.

Information already incorporated into model training may continue to have an effect even after the source file is deleted, because input data is statistically reflected in many model parameters. Gartner said Machine Unlearning, a technique for removing learned information, also has practical limits. If a leak is confirmed, Gartner recommends checking AI training settings, blocking further inputs, changing exposed passwords and authentication details, asking the service provider to delete the data, and confirming whether server logs and backups are retained or deleted.

Gartner said companies should go beyond simply banning AI use. They should identify which services employees use and what information they enter, then establish approved services and data management standards.