See and govern shadow AI across the company
Track unauthorized AI usage, spot risky data flows, and apply practical guardrails so teams can use AI without leaking source code, IP, or customer data.
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Get real visibility into AI usage
Move beyond policy documents and discover which tools, teams, and workflows are actually sending data to external AI systems.
Set granular data policies
Allow safe use cases while blocking high-risk behavior such as pasting source code, customer data, or internal strategy documents into unapproved tools.
Create an auditable governance layer
Give security and compliance teams evidence of what was used, what was blocked, and where risk is trending across the organization.
Comparison
| Control area | Policy-only approach | Operational governance |
|---|---|---|
| Tool visibility | Assume employees follow approved-tool lists. | Track real AI usage patterns so security teams can see what is actually happening. |
| Data protection | Rely on broad warnings and annual training. | Apply practical rules based on user, team, data type, and destination tool. |
| Compliance evidence | Reconstruct incidents after the fact. | Keep ongoing logs and policy outcomes that support audits and investigations. |
Frequently Asked Questions
What makes shadow AI different from earlier shadow IT problems?
The adoption cycle is faster, the data-loss risk is higher, and employees can leak sensitive information with a single paste into a public tool.
Do governance programs have to block all AI usage?
No. The strongest programs separate approved low-risk use from high-risk workflows and enforce policies at that level of detail.
Who usually owns this problem?
Security and compliance teams are common buyers, but successful programs also involve IT, legal, and the business teams already using AI day to day.
Turn AI usage into a governed workflow
Replace assumptions with visibility, policy enforcement, and audit-ready evidence.
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