Every decision to reduce security data costs also changes what analysts and AI can detect. Cloud activity, identity behavior, network flows, application events and historical context are often retained at different levels of fidelity, with little record of the coverage surrendered along the way. An AI-powered investigation can still produce a polished conclusion when a critical signal is missing. The analyst reviewing it may never know that the evidence was incomplete.

In this roundtable, we explore how security teams determine which telemetry belongs in the analytics pipeline, which data can move to a lower-cost tier and how quickly historical context must be available during an investigation. Participants will examine how to measure the security value of a signal, expose gaps to analysts and AI agents, and build a data strategy that can withstand both budget review and post-incident scrutiny.