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Solution

Secure AI Data Pipeline

Build AI features on clean data: nothing regulated goes in, nothing sensitive comes out.

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The problem

AI teams move fast and grab whatever data is available. Without controls at the pipeline level, regulated data ends up in embeddings, fine-tunes, and eventually in someone's completion.

How it goes wrong

A support-bot team indexes the entire ticket archive into a vector store. Tickets contain card numbers and health details. The bot now retrieves them for anyone who asks nicely.

How teams run it

Gate ingestion

Classification runs before embedding or training; regulated spans are redacted or the record is excluded.

Audit sets

Fine-tuning and RAG corpora get a signed audit: what data classes are inside, from which sources.

Watch outputs

Completions are scanned for sensitive data as a safety net, with incidents linked back through lineage.

What you get

  • Pre-indexing classification gates for RAG pipelines
  • Training-set audits before every fine-tune
  • Lineage from model artifacts back to source data
  • Output filtering as a second line of defense
Who owns it
AI/ML engineeringCISOData platform teamAI governance
Where it applies
DatabricksSnowflakeAWSGitHub
100%
of RAG documents classified before indexing
3.2%
of a typical corpus needs redaction before embedding

Products behind this solution

AI Security

Let your company use AI without your data training someone else's model.

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Data Classification

AI-native labels that read context, so a nine-digit number is a routing code in one file and a Social Security number in another.

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Data Lineage

Trace any sensitive record to its origin, every copy, and every pipeline it feeds.

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Make secure ai data pipeline a solved problem.

Walk through the operating model live, on your environments, with a security engineer.

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