Privacy-Aware Infrastructure in the AI-Native Era: An Asset Classification Case Study
Meta's asset classification system addresses noisy and probabilistic inputs in privacy-aware infrastructure (PAI) by applying a hybrid approach that combines machine learning models (LLMs) with deterministic rules. The system builds a rich context before classifying assets, using LLMs to handle ambiguity and novel signals, while human-reviewed labels ensure accountability and oversight. This approach enables PAI to reason under ambiguity while producing explanations and reproduceable results. By applying a decoupled evaluation loop and a deterministic-first pattern, the system improves accuracy and reliability. It defines a stable classification contract, builds a context mesh, and routes decisions through a funnel that prioritizes deterministic rules over LLM-generated recommendations. This hybrid approach shrinks the LLM's role in production over time, enabling low-latency, replayable, and auditable enforcement.