Definition
Governance involves trade-offs between control vs velocity, cost vs completeness, and privacy vs reproducibility. Understanding these tensions prevents compliance failures.
LATENCY VS AUDIT DEPTH
Synchronous logging of rich metadata adds 5-15ms per request. At 25K RPS, this is prohibitive. Solution: Async journals with minimal metadata (<5ms p99), generate explanations on-demand.
COST VS COMPLETENESS
At 10K RPS with 1KB logs, 864GB/day accumulates. 7 years online = 2.2PB. Solution: Tiered storage (30 days hot, 7 years cold). Trade-off: cold queries take minutes. Sampling low-risk at 10-20% reduces cost but creates forensic gaps.
💡 Insight: A rare fraud pattern might be missed if it falls in unsampled traffic. For high-stakes decisions, log everything; sample only low-risk.
PRIVACY VS REPRODUCIBILITY
Full feature snapshots enable reproduction but expose PII. Hashing + time-travel protects privacy but fails if data is purged. Differential privacy helps but may lose 2-5% AUC.
COMMON FAILURE MODES
Missing lineage: Unsigned datasets break reproduction. Shadow deploys: Teams bypass registry. Clock skew: Timestamp misalignment. RL drift: Continuous updates bypass reviews.
⚠️ Trade-off: No single configuration works for all. Tune governance depth by risk—stricter for lending/healthcare, lighter for recommendations.
✓Synchronous rich logging adds 5 to 15 milliseconds per request, production systems use asynchronous journals with p99 enqueue under 5 milliseconds and defer detailed explanations to batch or on demand to meet 50 millisecond Service Level Objectives (SLOs)
✓At 10,000 Requests Per Second (RPS), 7 years of full fidelity logs require 2.2 petabytes, tiered storage (30 days hot, remainder cold compressed) cuts this to under 1 petabyte but increases query time from seconds to minutes for historical investigations
✓Privacy versus reproducibility dilemma: logging raw features enables perfect reproduction but violates General Data Protection Regulation (GDPR), hashing with feature store time travel protects Personally Identifiable Information (PII) but reproduction fails if store is purged by retention policies or right to be forgotten
✓Differential privacy with epsilon equals 1.0 can degrade fraud model Area Under the Curve (AUC) by 2 to 5 percent, creating a utility cost for privacy guarantees that must be balanced against regulatory requirements
✓Missing lineage from unmaterialized datasets causes silent reproduction errors when late arriving data or schema changes alter row membership, mitigation requires signed immutable snapshots with cryptographic checksums
✓Online learning and Reinforcement Learning (RL) systems updating continuously can drift into noncompliance between reviews, requiring parameter change guardrails, update rate limits, and canary buffers that hold changes for manual review before full deployment
1Bank fraud model uses async journals with feature Hashed Message Authentication Code (HMAC), adds only 5 milliseconds to p99 latency, detailed Shapley Additive exPlanations (SHAP) values generated in batch overnight for regulator access within 24 hours, meeting both latency and audit requirements
2E commerce recommendation system samples 20 percent of low risk product suggestions (less than $50 value), logs 100 percent of high value recommendations (greater than $50), reduces storage from 1.5 terabytes per day to 400 gigabytes while maintaining full auditability for financially significant decisions
3Right to be forgotten request deletes user records from feature store at T plus 30 days per policy, reproduction for decisions before T plus 30 works (features still available), after T plus 30 reproduction fails (feature store purged), system retains prediction outputs and aggregate statistics but loses ability to regenerate exact intermediate states