- RingCentral had limited visibility into AI applications entering the environment, making it hard to understand AI adoption early.
- Teams couldn’t clearly see who accessed AI tools, what data was touched, or whether usage aligned with internal policies.
- Manual access reviews couldn’t keep up with the pace of AI adoption, increasing security and compliance exposure.
CloudEagle Reduced Access Risk with Just-in-Time Access Controls
“As AI tools quietly started entering our environment, we lacked a clear view of where AI was being used, which teams had access, or whether sensitive data was exposed. CloudEagle gave us immediate visibility into AI-powered applications, mapped AI access across users and teams, and helped us enforce clear guardrails so AI adoption stayed intentional, secure, and auditable instead of uncontrolled.”
~ Vaibhav Gupta, Head of Risk, Xendit
100%
AI App Visibility
30 minutes
To surface AI usage
1,500+
hours saved per year
100%
AI App Visibility
30 minutes
To surface AI usage
1,500+
hours saved per year
Challenge
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Solution
- RingCentral connected CloudEagle.ai to its environment and surfaced AI usage across the stack within 30 minutes.
- CloudEagle identified AI-powered applications, mapped access at the user level, and flagged unsanctioned AI tools.
- Automated access reviews and policy-based controls governed AI usage without slowing teams down.
Result
- Leadership gained immediate, continuous visibility into AI adoption across the SaaS stack.
- Shadow AI risk reduced by controlling who could access AI tools and how they were used.
- AI governance shifted from reactive oversight to proactive, audit-ready control.
Challenge
“As AI usage expanded across teams, the bottleneck shifted from visibility to execution. Access decisions were handled across Slack, tickets, and email, making approvals slow and ownership unclear.
At audit time, security and IT teams had to manually piece together who approved access, for how long, and under what conditions. Temporary access frequently turned into standing permissions, increasing risk and operational overhead.”
Solution
- Centralized AI access decisions that were previously scattered across Slack, email, and tickets.
- Enforced time-bound access at the point of approval to avoid perpetual permissions.
- Automated access reviews replaced manual follow-ups and spreadsheet tracking.
- Surfaced risky or policy-violating AI access as soon as it occurred.
- Captured approval context and access changes automatically for audits.
Why CloudEagle.ai?
- Slack- and ticket-based access workflows with approval orchestration and logging
- Native time-based access controls with automatic deprovisioning
- Policy-driven access reviews that run continuously in the background
- Real-time monitoring and alerting on AI app access and usage
- End-to-end audit logs covering approvals, access duration, and revocation
Impact
AI Tool Visibility and Control
- Teams moved from fragmented views of AI usage to a shared, organization-wide understanding.
- Blind spots across teams and tools were eliminated as AI adoption expanded.
- Leadership could assess AI adoption patterns without waiting for audits or reports.
Governance at Scale Without Manual Work
- AI governance doesn’t depend on manual reviews or periodic cleanups.
- Governance stayed consistent even as new AI tools entered the environment.
- Security and IT shifted from catch-up mode to steady-state governance.
Risk Reduction and Accountability
- Early alerts enabled teams to act on risky or non-compliant AI tools immediately.
- Clear ownership and time-bound access reduced exposure to data misuse.
- RingCentral moved from reactive audits to proactive AI oversight.
The Transformation
Before CloudEagle
AI adoption grew unevenly across teams, with no shared understanding of overall exposure.
Access decisions were fragmented, making ownership and accountability unclear.
Governance relied on periodic reviews, often triggered only during audits or incidents.
Security teams reacted to issues after risk had already accumulated.
AI governance was seen as a blocker or a cleanup task.
After CloudEagle
Teams operated from a single, consistent view of AI usage across the organization.
Access ownership became explicit, time-bound, and easy to trace.
Governance became continuous and built into everyday operations.
Risks were addressed early, as AI usage evolved.
Governance supported safe AI adoption without slowing teams down.
