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New · Covered by TechCrunch: MELURNA traces hidden third-party data flows.Read the research
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For EnterprisesUnify cyber, privacy, AI, and third-party risk around observed data movement.

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For Cyber InsurersSupport underwriting, portfolio, claims, cyber, privacy, AI, and risk-engineering decisions.

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Security & Enterprise RiskPrioritize threats by the sensitive data, vendors, and business impact they can reach.Privacy, Legal & ComplianceCompare sensitive-data behavior with consent, disclosures, contracts, and obligations.AI GovernanceGovern AI by connecting every service to its data, purpose, recipient chain, and change history.

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FinanceMap customer and transaction data across vendors and regulated boundaries.HealthcareVisualize PHI journeys across apps, devices, analytics, and vendors.View All IndustriesExplore how Melurna applies across regulated and data-intensive industries.
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Data flow mapping

Map the completerecipient chain.

Follow sensitive data from its initiating surface through every observed handoff.

Map a data journey
Product evidence
Journey Change Monitor
Illustrative Melurna journey change monitor comparing a current sensitive-data path with the previously observed recipient chain
Origin → nthjourney mapping
Initiatorcausal context
Changepath comparison

Decision view

A line between systems is not a complete data journey.

Show what initiated the transfer, what moved, how it changed, who received it, and where the path went next.
Observed journeyAccount creation
Four handoffs
InitiatorAccount form

Customer submits details

First recipientApproved SaaS

Request contains account ID

DownstreamIdentity service

Hashed email added

DestinationAnalytics provider

Cross-border recipient

Value changesRaw emailHashed emailRecipient ownership and jurisdiction remain attached.

Map origins and initiators

Connect each observed transfer to the website, application, page, form, script, feature, or third-party service that initiated it.

  • Collection surface
  • Initiating script or service
  • Customer-journey context
Illustrative Melurna vendor-chain record showing an approved vendor, a downstream AI service, sensitive data, disclosure status, and preserved evidence
Vendor Chain

Classify what moved

Describe the personal, health, financial, account, transaction, session, device, or credential data present in the flow.

  • Sensitive-data categories
  • Raw and transformed values
  • Linkability context
Illustrative Melurna evidence methodology showing observation, recipient resolution, policy comparison, risk context, and preserved proof
Evidence Methodology

Resolve recipient chains

Follow the path from the first recipient into vendors, subprocessors, infrastructure, AI services, and other downstream destinations.

  • Recipient ownership
  • Party depth and role
  • Geography and sovereignty
Illustrative Melurna compliance drift view comparing approved statements with current observed data journeys and material changes
Compliance Drift

Compare paths over time

Compare observed journeys with disclosures, consent choices, approved architecture, and previous observations to identify meaningful drift.

  • Stated-versus-observed gaps
  • Consent and GPC states
  • Material path changes
Illustrative Melurna recipient inventory showing third parties, AI services, material flows, regions, and a relationship drawer
Recipient Inventory

Continue exploring

Third-party privacy risk managementManage third-party privacy risk.Cyber threat intelligence platformThreat intelligence that follows the data.AI third-party risk managementManage AI risk beyond contracts.

Data Risk Review

Build the map around what the data actually did.

Start with one public-facing journey or vendor relationship.

Map a data journey Start with one application, workflow, vendor, or domain.