AI promises total automation for security investigations, but the reality is that teams are hitting a wall: investigations remain agonizingly slow. The failure isn't the AI, it’s the data feeding the machine. AI can’t accelerate a workflow when telemetry is unstructured or scattered across a dozen disconnected tools. It is enriched far too late in the cycle. Until that foundation changes, AI simply moves faster through the same old bottlenecks.
When more data creates less clarity
As environments scale across cloud and hybrid infrastructure, telemetry volumes explode, flooding existing platforms with logs and metrics. This drives up costs and stretches architectures that were never built for this level of scale.
To survive the deluge, teams make desperate tradeoffs. They sample data. They delay enrichment. They silo pipelines by tool. The result is a mess of noise and rising operational friction. Data arrives without the structure and context that modern investigations require.
Human-era tools meet AI-era demands
Most security stacks were designed for manual workflows. Humans queried logs, pivoted between tools, and assembled timelines by hand.
AI operates on a different plane. AI applications can make a hundred fold queries compared to human operators. Serving that type of retrieval volume benefits from structuring data when it's ingested so it can be enriched before analysis begins. When context is added late, it’s difficult for AI to reason effectively, creating inefficiencies.
From chasing logs to starting with answers
The core challenge is fundamentally changing where the investigation starts..
Traditional investigations start with frantic questions. Where do I look? What is missing? Modern investigations start with answers. They utilize telemetry that already includes the signals and structure needed to move toward a root cause. That shift depends on treating telemetry as a foundational layer. You must prepare data before incidents happen.
Why leaders are rethinking their stacks in 2026
AI-driven workflows are increasing data demands while costs continue to spike. No AI layer can compensate for a brittle telemetry architecture. The real leaders are separating data control from consumption. They build flexible pipelines that shape and enrich telemetry once. This supports many tools at once.
The future of AI depends on data that is AI-ready from the start.
How ready is your data for AI-driven investigations?
AI success depends on the foundation beneath it. Take the AI Readiness Assessment to see how prepared your telemetry and workflows are for the AI era and pinpoint the gaps that will slow you down.