Building Privacy Aware Infrastructure in the AI-Native Era#

Rituraj Kirti (Meta)

• Why: Not about networking or AI infra per se — but it presents the most transferable AI systems architecture in the program: how to run an LLM-driven control loop in production while converging it to deterministic, auditable, near-zero-LLM operation. That pattern applies far beyond privacy, e.g., to policy enforcement and classification over network/infra assets.
• What: A case study in AI-native asset classification at the scale of millions of data assets: a hybrid deterministic-rules engine with LLM fallback that converts messy context (metadata, lineage, code references, scan signals) into enforceable privacy controls — with ground-truth + eval self-improvement loops and a distillation step that exports high-performing LLM behavior into an auditable rules.yaml, driving LLM usage toward near-zero while improving determinism, latency, and cost. (from abstract only)
• How: Active learning focuses human review on high-value examples; LLM-as-Judge provides scalable review signal (with kappa reliability reported); evaluation gates prevent regressions and circular reinforcement; the converged behavior is distilled into declarative rules with drift monitoring in production.
• Where applicable: The privacy use case is Meta-specific, but the loop design — LLM with fallback, eval gates, distillation to declarative rules, drift monitoring — is a general recipe for any "LLM in the infra control path" system where you eventually need auditability and predictable latency/cost.