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How a Financial Services Enterprise Eliminated High-Risk AI Applications Before They Became Threats

“We had a sanctioned AI list of 26 vendors. CloudEagle surfaced 115 tools in use in the first ten days, each one scored for data exposure, DPA status, and subprocessor risk. Four of them were processing regulated customer data without a signed agreement. We found the threats before our next audit found them.”

- CISO, Financial Services Enterprise.

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89
high-risk AI apps identified
10 days
time to full AI inventory
100%
sanctioned apps with DPA

89

high-risk AI apps identified

10 days

time to full AI inventory

100%

sanctioned apps with DPA
Problems
Challenge
  • AI applications entered the environment through IDE plugins, browser extensions, and personal credit cards, outside the visibility of SSO and CASB tools.
  • Security evaluated each AI tool case by case with no repeatable risk rubric, so the same vendor was reviewed differently by different analysts.
  • The API tokens AI agents used to connect to other systems had never been reviewed, and usage-based billing made it impossible to review it

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Solutions
Solution
  • CloudEagle discovered AI applications across the SaaS and browser layer, including tools accessed through IDE plugins and direct URLs outside SSO.
  • GenAI Risk Scores applied a per-vendor rubric covering data exposure, DPA status, subprocessor risk, and regulatory alignment.
  • Privileged Access Visibility extended to non-human identities, with AI agent tokens and service accounts brought into the same review scope as human users.

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Profit
Result
  • 89 unsanctioned AI apps surfaced within 10 days, including four handling regulated customer data without a signed DPA.
  • Every AI vendor in the environment received a risk score in a single view, with 12 high-risk tools retired before the next audit window.
  • API tokens tied to AI agents were rotated or scope-reduced in the first review cycle, with continuous review established from that point forward.

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Challenge

At our financial services firm, AI tool adoption spread rapidly across teams, coding assistants, document summarizers, and support copilots, often entering through plugins, browser extensions, and personal purchases. While 26 tools were approved, many more operated unseen, creating significant blind spots.

The risk escalated with usage-based billing, where activity didn’t map to licenses, and AI agents held high-level access without any review. Sensitive data flowed through these tools with little visibility or control.

When leadership asked about data exposure, vendor risks, and compliance, the security team had no clear answers. The real risk: unknown AI tools accessing critical data without oversight, turning into potential audit findings or security incidents.

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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 platforms could not see.
  • GenAI Risk Scores applied a per-vendor rubric covering data exposure, DPA status, and regulatory alignment.
  • 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.
  • Spend Intelligence tied token and API consumption to contract terms, so usage-based cost spikes surfaced as risk signals alongside the vendor risk score.

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Why CloudEagle.ai?
  • Unified AI discovery across SaaS, IDE plugins, and browser extensions, including consumer-tier tools that SSO-only and CASB-only platforms could not see.
  • Per-vendor GenAI Risk Scores replaced manual analyst rubrics, so every AI tool was evaluated against the same data-exposure, DPA, and subprocessor criteria.
  • AI governance, SaaS governance, and identity governance in one control plane, so AI did not require a separate inventory tool or a separate security stack.
  • Non-human identity governance applied to AI service accounts and API tokens, not treated as an afterthought of the human-user stack.
  • Consumption-aware spend visibility tied token and API usage to contract terms, so cost anomalies surfaced as governance signals rather than quarterly finance surprises.

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Impact

Repeatable AI Risk Evaluation

  • AI vendor risk review throughput increased as the GenAI Risk Score replaced case-by-case analyst evaluation with a single rubric.
  • Every AI tool in the environment received a risk score in one view, visible to security, procurement, and legal at the same time.
  • AI intake moved from a 5-week security evaluation to a defined workflow with stage owners and score-driven routing.

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Reduced AI and Data Exposure

  • 89 unsanctioned AI tools were identified and sorted into sanctioned, renegotiated, or retired categories within the first month.
  • Four AI tools processing regulated customer data without a signed DPA were blocked or renegotiated before the next audit window.
  • API tokens tied to AI agents were rotated or scope-reduced through the first automated review cycle, removing standing production access.

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Sustained AI Governance Posture

  • New AI tool requests now route through a single intake workflow tied to the sanctioned vendor list and the DPA registry.
  • Continuous access reviews replaced annual reviews, with evidence captured as a byproduct of every access decision.
  • Board-level AI risk reporting moved from quarterly manual pulls to a live dashboard with vendor scores and DPA coverage.

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The Transformation

Before CloudEagle
AI vendor evaluation conducted case by case with no consistent risk rubric across analysts.
AI app inventory tracked across spreadsheets, Slack threads, and security intake tickets.
AI vendor DPA status and subprocessor lists recorded in legal team folders, separate from security systems.
API tokens and service accounts used by AI agents outside human-user access review scope.
Board questions about AI vendor risk answered through manual document pulls and cross-team email threads.
After CloudEagle
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Every AI vendor scored on the same GenAI Risk rubric covering data exposure, DPA status, and subprocessor risk.
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Single source of truth for every SaaS and AI application in use across the organization.
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DPA status, subprocessor list, and data-residency terms recorded alongside the AI vendor risk score in one governance view.
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API tokens and service accounts for AI agents included in continuous access reviews with scope and rotation rules applied.
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Board questions about AI vendor risk answered from a live dashboard with vendor scores and DPA coverage attached.

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