Uber ML platform data lineage audit — fragmented provenance across 30+ feature stores
Date: 2024-06
Affected: Uber Michelangelo ML platform — safety-critical features (ride pricing, driver matching, fraud detection) affected by unauditable training data provenance
Attack vector: other
Category: real-world
Severity: High

Uber's internal ML platform audit (referenced in their 2024 engineering blog series) revealed that the company's Michelangelo ML platform had accumulated over 30 distinct feature stores, model registries, and data pipeline systems across different teams, with no unified lineage tracking. Data scientists could not trace which training data contributed to which production model, creating regulatory and debugging blind spots. Model predictions in safety-critical features (ride pricing, driver matching, fraud detection) could not be audited back to their training data sources. The audit led to a multi-year consolidation project. This represents the canonical example of data lineage fragmentation at scale — the exact risk described by DSGAI06.

Impact: Regulatory audit compliance impossible without manual investigation; debugging production model issues required weeks of manual data tracing; multi-year consolidation project required; demonstrates that data lineage fragmentation is inevitable without governance from day one

Mitigations:
Unified data catalog with automatic lineage capture from day one
Mandatory model cards with training data provenance for every production model
Centralized feature store with versioning and access logging
Regular data lineage audits — annual at minimum for regulated applications

Architectural layers (MAESTRO):
label: Data Operations
notes: 30+ disconnected data stores with no unified lineage
role: origin
label: Evaluation & Observability
notes: No observability across fragmented pipeline — auditors could not trace data to model
role: blind-spot
label: Security & Compliance
notes: Regulatory audit requests could not be satisfied without manual investigation
role: blind-spot