CUSTOMER CASE STUDY
How Unsanctioned AI Fell to Near Zero at a Fortune 500 Healthcare Technology Enterprise
A global Fortune 500 healthcare technology enterprise used Aurascape to secure its sanctioned AI tools, set guardrails for medium-risk AI use, and block the long tail of risky new AI apps and agents.
Unsanctioned AI access
Long-tail AI and personal-account use fell to near zero after enforcement, with users routed to sanctioned tools.
Users governed worldwide
AI governance for a global workforce across the U.S., LATAM, APAC, UAE, EU.
Sensitive data protected inline
Proprietary and confidential data categorized and protected in real time across AI surfaces.
AURASCAPE
Phased Approach
Visibility
Build a complete inventory of AI apps, agents, and embedded AI, with risk-scoring and usage analysis.
Control
Sanction approved tools, tolerate medium-risk tools with usage policy guardrails, and block the long tail of unnecessarily risky AI tools.
Protection
Apply data and threat policy to sanctioned enterprise AI tools, with inline policy enforcement.
CUSTOMER CONTEXT
Global Fortune 500 Healthcare Technology Enterprise
With a large, distributed global workforce, new AI tools entered this organization faster than any team could manually track. Existing firewall and SASE solutions struggled to categorize AI URLs accurately, let alone control usage. The security team could see destinations but not AI interactions, and set out to close that gap before it became a blind spot.
About the Customer
- Industry
- Healthcare Technology
- Number of Users
- 60,000+
- Global Footprint
- U.S., LATAM, APAC, UAE, and EU
- AI Policy Model Tiers
- Sanction, tolerate with guardrails, block
CHALLENGES
What the Enterprise Needed to Secure
Identify the AI in Use
New public AI apps, embedded AI in SaaS and websites, and free-tier accounts arrived faster than manual review could catalog them. Existing controls miscategorized AI URLs, creating more manual work.
Block Long-Tail AI as it Appears
The vast majority of new AI apps carried real risk and no business need. But existing security controls and manual review could not keep pace with their release.
Protect Sanctioned AI Usage
Employees could reach a sanctioned tool through the enterprise tenant or a personal free-tier account. Destination-based controls treated both as the same destination, leaving the license boundary unenforced.
Set Guardrails for AI Tools in the Middle
Several tools sat between clearly sanctioned and clearly too risky for work. They were generally safe, as long as no sensitive data left and certain modes stayed off.
OBJECTIVES
- Maintain a complete, current inventory of AI apps and agents in use, risk-scored automatically as new ones appear.
- Keep the majority of AI use inside approved, licensed enterprise tools, with personal-account access redirected to sanctioned tenants.
- Block unnecessary, high-risk long-tail applications by risk score, without manual review of every new app.
- Tolerate medium-risk tools within guardrails: granular policy based on context, mode, and usage.
- Enforce data and threat policy on sanctioned enterprise AI tools, protecting sensitive data and stopping threats in AI responses.
OUTCOMES
Unsanctioned AI access fell to near zero while governed AI use grew to 60,000+ users worldwide.
Use outside licensed access also fell to near zero, sensitive data stayed protected inline, and the enterprise extended the same governance model across their global workforce.
WHY AURASCAPE
One catalog of AI risk, mapped to tiered policy and enforced in real time.
Aurascape secures how employees and agents use AI. It discovers known and long-tail AI applications and scores the risk of each one. New applications inherit the right tier from your acceptable use policy: sanction, tolerate with guardrails, or block. Controls run in the interaction path, so users keep working and security keeps evidence.
Complete AI Discovery
Finds known and long-tail AI applications and agents, and risk-scores each on behavior, permissions, and data handling.
AI-Native Data Protection
Real-time data classifiers identify PII, source code, and confidential business data inline. Redact before data reaches a sanctioned AI model, or block it from leaving for an unsanctioned tool.
Entitlement Enforcement
Distinguishes sanctioned enterprise tenants from personal accounts, so policy follows how a tool is used, not just where the traffic goes.
Inline Enforcement & User Coaching
Allow, coach, warn, block, or redact as the interaction happens. Coaching reaches users in the browser and beyond it, including development environments and the command line.
Zero-Bypass MCP Gateway
Discovers MCP servers and tools, inspects prompts, responses, and tool calls, and enforces policy on the actions agents take and the data they reach. Calls that try to route around it arrive unsigned and are blocked by default.
Audit-Ready Evidence
Interaction records show who used AI, what data was involved, what policy applied, and what happened next. Role-based access control governs who can review them.
Bring AI Under Control
See how Aurascape finds the AI in use, scores its risk, and enforces the right policy for each tier.