Platform / AI data exposure
AI Data Exposure
Sensitive data leaks through AI two ways - in the prompts and responses crossing the wire, and through the corpora a RAG pipeline can reach. Penaxtra covers both.
Last reviewed June 2026
Problem
The gap AI data exposure closes
Customer records, secrets, and PII end up in prompt bodies sent to third-party models, and in RAG corpora a retrieval can surface across tenants. Neither is visible to classic DLP, which never sees the LLM path.
How Penaxtra approaches it
How Penaxtra delivers AI data exposure
At runtime, the self-hosted gateway applies DLP rules and redaction to prompts and responses on the wire, so PII and secrets are scrubbed before a request leaves your network. At rest, Penaxtra inventories the data sources, RAG corpora, and vector stores in scope and tests retrieval boundaries for cross-tenant leakage. Judge rationales are PII-redacted before any record is persisted.
Technical capabilities
AI data exposure capabilities
Secrets and PII scrubbed before egress to model providers
Data-source, RAG corpus, and vector-store inventory
Retrieval-boundary and tenant-isolation tests
PII redaction before any finding is stored
Findings mapped to data-governance control IDs
Compliance mapping
AI data exposure compliance mapping
Maps to OWASP LLM06 (sensitive information disclosure), EU AI Act Article 10 (data governance), GDPR data-minimisation expectations, and ISO/IEC 42001 data controls.
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