CloudEagle.ai Launches AI Governance to Help Enterprises Discover Shadow AI, Enforce Usage Policies, and Stop AI Risk

Gartner found 57% of employees use personal GenAI accounts for work, and 33% input sensitive data into unsanctioned tools.

CloudEagle.ai today launches AI Governance to help enterprises discover shadow AI, enforce approved AI usage, assess GenAI and NHI risk, and manage AI spend with token consumption insights. The solution addresses growing concerns around sensitive data exposure, compliance risks, and unmanaged AI adoption across the workplace.

CloudEagle.ai gives us visibility into both the data going in and the usage patterns behind it, so we can support adoption without losing sight of what’s happening underneath.”

— Edward Hausauer, Technology Operations Manager, Pioneer Schools.

AI tools are already inside the enterprise. They arrived without procurement reviews, security sign-offs, and, in many cases, IT’s knowledge. The question is no longer whether employees are using unapproved AI tools. The question is how long governance can lag before it becomes a liability.

When an employee signs into ChatGPT or Perplexity on a work device, that session represents sensitive data entering a system nobody reviewed, a license nobody tracked, and an access event nobody logged. CloudEagle.ai moves governance to the moment behavior happens, so enterprises are not catching up to adoption; they are keeping pace with it.

Also Read: AiThority Interview with Matej Bukovinski, Chief Technology Officer at Nutrient

Explore the full AI Governance platform at CloudEagle.ai. Enterprises gain visibility and control across their entire AI footprint:

1. Shadow AI discovery: Automatically identifies every AI tool being used across the organization, including unapproved applications, the moment adoption begins.
2. GenAI risk scoring: Evaluates AI vendors based on data training policies, feature controls, certifications, and compliance posture so security teams can assess risk before approval.
3. Token consumption and usage tracking: Provides visibility into models, token usage, teams, and spend so Finance and IT can align AI costs to actual business usage.
4. MCP server governance: Surfaces active MCP servers, ownership, permissions, and connected workflows to close emerging governance gaps around AI agents and orchestration layers.
5. Real-time usage enforcement: Intercepts access to unapproved AI tools with policy-based guidance and redirects users to approved alternatives at the moment behavior occurs.
6. Secure browser controls: Monitors and blocks sensitive data exposure to AI tools at the browser layer before information leaves the organization.
Audit-ready access logs: Automatically capture every AI access event to create a continuous evidence trail for compliance and governance reviews.
7. AI spend and license visibility: Consolidates visibility into duplicate tools, underutilized licenses, renewals, and AI spend to support data-driven optimization decisions.

For organizations already dealing with the consequences of ungoverned AI adoption, the gap between what teams are using and what IT can see has been the hardest part to close.

“We didn’t always know what data was being fed into these tools or how teams were actually using them, and that’s a hard gap to close once it’s wide open. CloudEagle.ai gives us visibility into both the data going in and the usage patterns behind it, so we can support adoption without losing sight of what’s happening underneath,” said Edward Hausauer, Pioneer Schools.

A Gartner survey of 302 cybersecurity leaders found that 69% of organizations suspect or have evidence that employees are using prohibited public GenAI, with IP loss and data exposure among the cited risks. Organizations that establish governance now will be in a stronger position than those attempting to retrofit controls after adoption has already scaled.

“AI spend is unlike any other software cost; it scales with usage in ways a contract never captures. CloudEagle.ai gives enterprises visibility into token consumption, license utilization, and shadow AI in one place, so the bill at the end of the month is never a surprise.”
— Nidhi Jain, CEO and Founder, CloudEagle.ai

Also Read: ​​AI systems – Interoperable AI systems: Connecting models across platforms