Cisco Articulates Splunk Security Data Optimization Architecture Principles

The core argument is that in Splunk security architectures, data optimization should be driven by detection requirements, not storage costs. Common mistakes include making retention, index, or filtering decisions before detection engineering matures, leading to lost coverage in ES correlation searches and degraded Risk-Based Alerting (RBA) due to missing historical context.

The author proposes a detection-driven optimization framework: classify data sources by analytic role (Detection-Critical, Investigation-Critical, Baseline-Critical, Compliance-Only), then map them to Splunk's Active, Selective, and Archive storage tiers. Key success KPIs should be improvements in Mean Time to Respond (MTTR) and stable detection coverage, not cost per GB.