Shadow AI

Shadow AI refers to the use of artificial intelligence tools — chatbots, code assistants, image generators, and other AI-powered applications — by employees or teams without the knowledge, approval, or oversight of IT and security teams. It is the AI equivalent of shadow IT, and it creates a distinct category of data security risk that most traditional security controls were not designed to address.

Common examples include employees pasting customer data into ChatGPT to draft a summary, engineers using an unapproved AI coding assistant that logs prompts to an external server, or a business unit deploying a third-party AI tool that processes proprietary financial data without a data processing agreement in place.

Why shadow AI is a data security problem

The risk is not AI itself — it is the uncontrolled movement of sensitive enterprise data into AI systems that security teams cannot see. When an employee enters a customer record, a contract, or a set of credentials into a public AI tool, that data may be used to train the model, stored on the vendor's servers, or accessible to other users in ways that violate data privacy laws and internal security policies.

Research from Cyberhaven Labs found that 39.7% of the data employees share with AI tools is sensitive. That number will rise as AI tools become more deeply embedded in everyday workflows.

The compounding problem is scale. Shadow AI is not a single rogue employee — it is a systemic behavior across an organization. Security teams cannot monitor what they cannot see, and most existing DLP tools were not built to inspect AI prompts and outputs.

Shadow AI vs. shadow IT

Shadow IT typically involves unsanctioned applications: a team using Dropbox instead of the approved file-sharing tool. The data exposure risk is real but usually bounded — the data goes somewhere specific and can often be retrieved or governed retrospectively.

Shadow AI is harder to contain. Data entered into a prompt is consumed by the model in real time. There is no file to locate, no server to disconnect from. The data may already have been processed, stored, and potentially incorporated into model outputs before security teams are aware it happened.

How to detect and manage shadow AI

Effective shadow AI governance requires visibility at two layers. First, discovery: identifying which AI tools are in use across the organization, including tools that employees have connected to enterprise data stores via OAuth or API integrations. Second, monitoring: tracking what data is entering AI prompts and what sensitive content is being surfaced in AI-generated outputs.

Data Security Posture Management (DSPM) platforms with AI data security capabilities can identify AI tools that have been granted access to sensitive data stores, flag when regulated data — PII, PHI, intellectual property — is flowing into AI workflows, and trigger alerts or automated responses when policy violations occur.

The goal is not to block AI adoption — it is to make that adoption visible and governable so that the productivity gains from AI do not come at the cost of data security.

Sentra discovers and monitors AI tools that have access to your sensitive data, including shadow AI deployments teams haven't disclosed. [See Sentra for AI & ML →]

‍