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Connect sensitive-data movement to vulnerabilities, third-party risk, downstream dependencies, and portfolio concentration. Prioritize threats by material impact.
Why the evidence matters
Combine vulnerability intelligence with observed data movement, vendor ownership, fourth-party reach, and portfolio concentration to identify material threat exposure.
Evidence changes the threat model
Consumer products routinely send personal data to machine-learning systems operated by advertising, identity-resolution, behavioral-analytics, session-replay, and fraud-scoring vendors. The movement can begin as soon as a customer signs up, signs in, or checks out.
A page tag can suggest intent, but it cannot establish whether an email actually left the company, whether the value was transformed, or which downstream recipients received it. Melurna connects observed data movement with vendor, threat, regulatory, and portfolio context so teams can prioritize the exposures that can cause material harm.
Three intelligence feeds
Identify what sensitive data leaves the company, in which form, and to whom, with the regulatory tripwires clearly identified.
The third-party data supply chain behind a company, including where a single vendor issue can become correlated loss across a portfolio.
A tiered view of embedded third parties that apply machine learning to user data, giving AI-governance teams evidence beyond the application inventory.
Beyond a traditional threat feed
A critical vulnerability becomes more urgent when the affected vendor receives sensitive customer, employee, health, payment, or credential data. CVE severity, exploit activity, data sensitivity, and recipient role can be prioritized together.
An approved vendor may forward data to subprocessors, identity partners, cloud services, or embedded AI providers. Downstream visibility shows when a fourth party can create risk for the enterprise.
A shared recipient across many companies, insureds, or business units can become a correlated-loss event. Concentration intelligence reveals the dependencies hidden inside otherwise separate risks.
When a recipient reports an incident, teams can identify the affected companies, journeys, and data classes, then focus containment and notification decisions on the relationships that carried material data.
Threat priority changes when sensitive data reaches a high-risk jurisdiction or crosses a regulated border. Recipient location adds legal and geopolitical context to the same exposure.
AI exposure often appears inside advertising, analytics, identity, fraud, and marketing services rather than direct chatbot or model-provider calls. Threat intelligence should include that embedded ML layer.
Compare the recipient chain with privacy notices, vendor inventories, contracts, and approved processing purposes to identify relationships that are missing, incomplete, or materially different.
Prioritize the vendors and companies with proven sensitive-data access during third-party review, M&A diligence, underwriting, renewal, and portfolio triage.
A critical vulnerability or vendor incident becomes more urgent when the affected recipient handles sensitive customer, employee, health, payment, or credential data.
Identify when an approved vendor extends exposure to subprocessors, identity partners, infrastructure providers, or embedded AI services outside the contracted relationship.
Reveal shared recipients across companies, insureds, business units, and customer journeys so a single vendor weakness can be evaluated as a correlated-loss event.
Give security, privacy, AI governance, enterprise risk, and insurance teams a common view of the data, recipient, threat, and business impact.
Data Risk Review
Start with one company, vendor, or portfolio.
Request an evidence briefing Review slots are scheduled in the order requests are received.