AI governance

Govern AI fromobserved use.

Discover declared and hidden AI services, trace the sensitive data and downstream providers involved, and compare observed use with approved purpose.

Review observed AI use

Product evidence

Illustrative Melurna AI control posture showing vendor exposure, data leakage, disclosure, sovereignty, and evidence changes — AI Control Posture

Declared ↔ ObservedAI inventory comparison

Data + Purposegovernance context

1st → nth partyAI recipient visibility

Decision view

An inventory names the service. Governance needs the data path.

Connect each AI service to its initiator, data categories, recipient chain, location, policy context, and owner so review follows actual use.

Declared useCustomer-support assistant

Approved purpose and owner recorded

Observed data path

Support form AI enrichment Model gateway

Account IDMessage contentSession data

Governance reviewDownstream provider added

Compare with approved purpose and disclosure

Industry perspective

We checked out all these platforms. There's like nothing that does what Melurna does.

Discover Shadow AI

Identify AI services, model endpoints, inference patterns, embedded copilots, and AI-enabled vendor calls outside the declared inventory.

Hidden AI recipients
Embedded AI features
Model and inference endpoints

Illustrative Melurna Shadow AI policy record showing evidence, monitoring, policy gaps, ownership, and review status — Shadow AI Policy Record

Understand the data and purpose

Show which sensitive-data categories travel with the AI request and connect the behavior to the surface, feature, or vendor relationship that initiated it.

Data sensitivity and linkability
Initiating surface context
Approved-purpose comparison

Illustrative Melurna recipient relationship showing the initiator chain, sensitive-data evidence, disclosure status, and monitoring — Recipient Relationship

Follow the AI supply chain

Trace the relationship beyond the named vendor to infrastructure, subprocessors, model APIs, and cross-border recipients.

Downstream AI providers
Ownership and processing role
Sovereignty and concentration

Illustrative Melurna compliance drift view comparing approved statements with current observed data journeys and material changes — Compliance Drift

Operationalize AI governance

Route observed AI use into approval, exception, remediation, policy, and recurring review workflows with traceable evidence.

AI policy alignment
Material-change review
Remediation verification

Illustrative Melurna journey change monitor comparing a current sensitive-data path with the previously observed recipient chain — Journey Change Monitor

Data Risk Review

Govern observed AI use, not inventory alone.

Start with one enterprise, AI provider, or vendor portfolio.

Review observed AI use Start with one AI vendor, application, or public customer journey.