Data Lineage Poisoning

Signal #008: Data Lineage Poisoning

Questions to Consider

• “What is the 'Integrity Hash' of our knowledge base? Do we know if a document was edited between the last audit and this specific query?”
• “Are we mixing 'Public Web Data' with 'Private Corporate Truths' in the same vector space, allowing external bias to poison internal logic?”
• “Is there a 'Mechanical Quarantine' for new data intake before it is promoted to the AI's active memory?”

Expected Excuses

• "The RAG system is 'Live' and self-updating; we cannot verify every single file in real-time." — Rebuttal: A 'Live' system that consumes unverified data is a 'Poisoned' system. We require a 'Staging Area' for all data before it is 'Promoted' to the AI's active memory. Freshness without Verification is a fiduciary liability.
• "The LLM is smart enough to detect contradictions in the data and flag them internally." — Rebuttal: LLMs are designed for pattern matching, not objective truth-seeking. If the underlying pattern is poisoned or contradictory, the output will follow that drift. Trust the cryptographic hash, not the model's 'intuition'.

The Friction

The desire for 'Real-Time Insight' creates a 'Validation Gap.' Technical teams prioritize ingest speed over data pedigree to meet 'Agile' deadlines. Signal #008 mandates a 'Mechanical Quarantine' for all incoming knowledge, ensuring the AI never 'learns' from an unvetted or adversarial source that could skew financial or legal outcomes.

The Function: The Integrity Funnel (RAG-IF)

A multi-stage intake protocol that prevents 'Economic and Logic Drift' by ensuring every piece of data used in an AI prompt has a verified integrity signature and a documented lineage.