Whitepaper
Caber Data Triangulation
How Caber identifies data fragments with certainty
No single signal, source metadata, semantic similarity, network logs, or classification, can reliably identify a data fragment. But when multiple noisy signals are correlated, their errors cancel and identification becomes precise. This is triangulation: the same principle that makes GPS accurate. This paper explains why single signals fail, how observing data-at-rest, data-in-motion, and data-in-use together enables definitive identification, and how Caber implements triangulation at scale.
Key Insights:
- Why single signals fail in predictable ways
- How triangulation cancels noise through correlation
- The need to observe all three data states
- Compounding accuracy of the context graph over time
