How a Fortune 500 Company Surfaced Shadow AI Tools and Built a Defensible AI Governance Program

“Our internal position was that we had 30 sanctioned AI tools. CloudEagle found 103 in the first two weeks, that was the moment our AI governance program moved from a slide deck to a defensible control set the board could actually review.”

- Chief AI Officer, Fortune 500

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shadow AI tools surfaced

high-risk AI tools blocked

100%

AI apps under policy

73

shadow AI tools surfaced

18

high-risk AI tools blocked

100%

AI apps under policy

Challenge

AI tools spread through browsers, IDE plugins, and personal accounts faster than they could be tracked.
When regulators and the board asked which tools handled sensitive data, there was no single source of truth.
Access reviews lagged, and service accounts, API tokens, and AI agents remained completely outside our visibility.

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Solution

CloudEagle discovered AI applications across the SaaS and browser layer, including tools accessed through IDE plugins and direct URLs.
AI Vendor & Contract Management centralized every AI vendor, DPA status, into one governance record.
User Access Reviews automated AI app review on a continuous cadence, with non-human identities brought into the same review scope as human users.

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Result

73 previously invisible AI tools surfaced and 22 were retired, replacing them with approved alternatives
100% of AI applications mapped to a data-sensitivity tier with vendor DPA status recorded and reviewable on demand.
AI access review cycle moved from annual to continuous, with service accounts and API tokens included in every cycle.

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Challenge

This Fortune 500 enterprise saw AI tools spread rapidly across teams, coding copilots, support assistants, research agents, and summarizers, often entering through browser extensions, IDE plugins, and individual purchases. Existing discovery tools only tracked apps behind SSO, missing many AI tools entirely.

While 30 tools were officially listed, the real footprint was unknown.

As more of these tools handled sensitive data, visibility gaps turned into risk. There was no clear view of which vendors had proper agreements, which tools processed regulated data, or which AI agents had high-level access. Even non-human identities like API tokens were largely untracked.

The API tokens AI agents used to connect to other systems had never been reviewed, and usage-based billing made it impossible to see when they were doing more than intended.

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Solution

Shadow AI & Shadow IT discovery across the SaaS and browser layer surfaced AI applications accessed through IDE plugins, browser extensions, and direct URLs that SSO-only tools could not see.
AI Vendor & Contract Management centralized every AI vendor record, DPA status, data-processing tier, and renewal date into a single governance view.
Application Rationalization flagged duplicate copilots and overlapping model subscriptions across business units, with usage data attached to each recommendation.
User Access Reviews ran continuously across sanctioned AI applications, with reviewer routing tied to HRIS data and manager hierarchy rather than spreadsheets.
Privileged Access Visibility extended to non-human identities, including API tokens and service accounts used by AI agents, bringing them under the same review scope as human users.

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Why CloudEagle.ai?

Unified discovery across SaaS and AI, including browser-based and IDE-based tools that SSO-only and CASB-only platforms could not see.
AI governance, SaaS governance, and identity governance in one control plane, so AI does not require a separate inventory tool or a separate security stack.
Per-application risk scoring mapped to regulatory control requirements without manual spreadsheet mapping every audit cycle.
Continuous user access reviews instead of annual fire drills, with reviewer routing, evidence capture, and de-provisioning built into the same workflow.
Non-human identity governance applied to AI service accounts and API tokens, not treated as an afterthought of the human-user stack.

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Impact

Defensible AI Governance on Demand

Complete AI application inventory available to the board, regulators, and internal audit within 14 days of deployment.
Every AI vendor mapped to DPA status, data-sensitivity tier, and signed contract date in a single governance record.
AI intake, risk review, and sanctioning moved from a 6-week manual process to a defined workflow with stage owners.

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Reduced AI and Data Exposure

Three AI copilots running inside production code repositories were identified and brought under sanctioned-tool controls.
18 AI tools handling regulated customer data without a signed DPA were blocked or renegotiated before the next audit window.
API tokens tied to AI agents were reviewed for the first time, with over-privileged credentials rotated or retired.

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Sustained AI Governance Posture

AI tool requests now route through a single intake workflow tied to the sanctioned vendor list and DPA registry.
Continuous access reviews replaced annual reviews, with evidence generated as a byproduct of every access decision.
18 high-risk applications without DPA were blocked, with access removed and budgets reallocated to the sanctioned AI.

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The Transformation

Before CloudEagle

AI tool inventory tracked across spreadsheets, procurement tickets, and business-unit-level AI committees.

AI vendor DPA status recorded in legal team folders, separate from security and procurement systems.

AI app access reviewed annually, with service accounts and API tokens outside review scope.

Duplicate AI tools and overlapping model subscriptions identified only at contract renewal.

Board and regulator questions about AI oversight answered through manual document pulls and email threads.

After CloudEagle

Single source of truth for every SaaS and AI application in use across the 14 business units.

AI vendor DPA status, data tier, and renewal date recorded in one governance view alongside SaaS contracts.

Continuous access reviews across sanctioned AI apps, with non-human identities included in every cycle.

Duplicate AI tools and overlapping subscriptions flagged continuously through Application Rationalization and Spend Intelligence.

AI governance reporting for board and regulators shifted from manual effort to real-time, audit-ready visibility with zero dependency on fragmented data sources.