AI Interaction Data Fabric Insights
Tenant-Wide AI Vendor Grant · Regulated-Data Exposure Scoped
September 23, 2026
AI Vendor Grant FAQs
Speak to an ExpertWhat is the AI Interaction Data Fabric?
The AI Interaction Data Fabric is the layer inside Kovrr's AI Security and Governance Platform that draws telemetry from every connected source into one analytical view, spanning network, identity, browser, endpoint, cloud, and the AI Vendor Risk Catalog. Signals that each source records in isolation get triangulated into a single dated and attributed finding. Exposure that stays invisible to any one console surfaces once the sources are read against one another.
What does signal triangulation mean?
Signal triangulation is the practice of reading telemetry from several independent sources against one another rather than trusting any single log. A paste captured in the browser carries little weight alone, and an identity resolved in the directory carries little weight alone. The exposure appears only where those separate readings overlap, which turns a set of benign events into one reportable finding.
How does the AI Interaction Data Fabric detect a risky AI vendor grant?
A tenant-wide consent to an AI vendor is a legitimate administrative action, so no single log reads its exercise as exposure. The directory logs the grant and the shared-account sign-in without naming the human behind the token. The directory activity shows the grant in use, 3,180 requests opening with full enumeration, without weighing what was pulled. The content audit names the files read while the Vendor Risk Catalog resolves the counterparty, a 19-day-old vendor with no verified publisher, no SOC 2, and no data-processing agreement. Triangulated, the sources establish that a permanent grant to an unassessed vendor is running through an account no human owns, with the catalog setting severity and the audit record setting scope.
Why does a shared service account break data attribution?
Auditors and regulators do not ask whether an access was logged. They ask who reached the data and whether that person was authorized. A shared service account answers neither, since the read is recorded against an ordinary user with no manager and no single human behind it, used across six devices and four networks in a month. The audit field is confidently populated, which is what makes it dangerous, because every downstream tool believes a real account name that points at no person. Resolving the account to a non-person is what turns a complete audit trail into an unanswered attribution question.
