AI Data Leakage Assessment

AI data leakage can come from model output, retrieval context, citations, logs, tool calls, connectors, or permissions that make sensitive content discoverable. SCS tests the exposure path and captures evidence.

AI Data Leakage Assessment.

Secure Consulting Solutions (SCS) provides an AI Data Leakage Assessment for LLM applications, RAG systems, AI agents, internal copilots, and Microsoft 365 Copilot exposure scenarios.

The assessment validates whether sensitive data appears through model output, retrieval context, citations, logs, tool calls, connectors, source paths, labels, or permissions that make content AI-discoverable.

Deliverables include sensitive category findings, evidence provenance, role or tenant exposure context where applicable, remediation guidance, and retest plans.

Assessment summary
Identity Which roles, tenants, personas, or workflows can receive sensitive AI output or metadata.
Evidence Outputs, retrieved docs, citations, tool calls, source paths, connector IDs, labels, and canaries.
Risk Data leakage, tenant leakage, metadata leakage, connector oversharing, logging exposure, and permission sprawl.
Deliverable Executive summary, technical report, findings CSV, evidence provenance, remediation guidance, and retest plan.

What this assessment answers

  • Can users receive sensitive content they should not discover?
  • Do citations, metadata, labels, or source paths reveal sensitive information?
  • Does RAG context bleed between users or tenants?
  • Do tools or connectors expose more data than expected?
  • Can logs or conversation storage retain sensitive content?

What we test

  • Model-output data leakage
  • RAG context bleed
  • Tenant leakage
  • Copilot-sensitive data exposure
  • Connector oversharing
  • Source and citation leakage
  • Secrets in context or configuration
  • Conversation storage and logging risks

Deliverables

  • Sensitive category findings
  • Evidence provenance
  • Role or tenant exposure matrix where applicable
  • Root cause context when inventory is provided
  • Prioritized remediation
  • Retest plan

Operator-led, evidence-first assessment.

The assessment distinguishes confirmed exposure from metadata exposure, expected access, likely oversharing, and inconclusive results so leadership can act without overstating the evidence.

AI data leakage assessment questions.

What counts as AI data leakage?

AI data leakage can include sensitive content in answers, retrieved context, citations, metadata, logs, tool calls, connector output, or source paths.

Do you distinguish expected access from exposure?

Yes. SCS separates confirmed exposure, metadata exposure, expected access, likely oversharing, and inconclusive results so remediation is tied to evidence.

Can you test role or tenant differences?

Yes, when scoped identities or representative accounts are available. SCS compares what different users, roles, tenants, or personas can receive.

What do you need from us?

Typical inputs include target access, test identities, approved sensitive categories, sample workflows, and optional inventory or access-path exports for root-cause context.