01 The problem
Legacy SIEMs are one welded block.
Collection, storage, detection and billing ship as a single platform. Change one and you re-platform all of it, while every byte is indexed and billed before anyone asks if it matters.
Abstract is the composable SIEM. Collection, detection, retention and AI SecOps run as independent layers, so you shape data before it's billed and keep the tools you already own.
01 The problem
Collection, storage, detection and billing ship as a single platform. Change one and you re-platform all of it, while every byte is indexed and billed before anyone asks if it matters.
02 Decouple
Four independent layers, each one swappable and each one scaling on its own. Data moves between them on your terms, not your vendor's.
03 Collection
Collect from every source, drop the noise at the edge, normalise to the schema you need and route each stream where it's worth paying for.
04 Detection
Detections run on data in motion, before it lands, then re-run across history when new intelligence arrives.
05 Swap any layer
Use Abstract's tiered retention or point the layer at your own lake. Nothing above or below it has to move.
06 AI SecOps
AI agents triage, enrich and investigate every alert with the full context the layers above have already shaped.
SIEM pricing scales with data volume, and security data grows 25–30% a year. Most of it is indexed at hot-storage prices and never queried by a detection.
“Our old model was ‘log everything and sort it out later.’ That worked until storage costs and performance caught up to us.”
Detection is tied to storage, storage to licensing, licensing to one vendor. Every upgrade is a rip-and-replace.
Critical telemetry and never-queried compliance logs are indexed, stored and priced the same way.
The bill tracks volume, not value. It grows every year; confidence that a real attack gets caught doesn’t.
Illustrative estate. Abstract’s research: 80% of stored data is never used for detection or response.
Abstract splits the SIEM into four layers that run on their own. Watch each one work on live data, then compose your own stack.
Layer 01 · Security data fabric
Collect from every source, then filter, normalize and route at the edge, so each stream lands where it’s worth paying for.
pipeline · edge-01 StreamingLayer 02 · Streaming + historical
Detections run on data in motion, before it is indexed, then re-run across history when new intelligence arrives.
detect · streaming Rule armedLayer 03 · Tiered storage
Tier data by what it’s for: hot for what you hunt, low-cost for what compliance needs. Point the cold tier anywhere; nothing above it changes.
retention · tiers Policy activeLayer 04 · Agentic triage
AI agents triage and investigate every alert with the context the pipeline already added, so people review conclusions, not raw logs.
ai-secops · case 4471 Agent onlineAll four · your order
Each layer runs on its own and plugs into the stack you already have. Switch one on today; everything else keeps working where it is.
stack.yaml · try it AppliedSIEM pricing scales with volume. Security data keeps growing, compliance keeps it for a year or more, and most of it never feeds a single detection.
Scroll the invoice. The beam runs the pipeline over each line and re-prices what it touches.
Scroll to apply the pipeline
Figures are illustrative. Abstract models yours on the demo call.
You don’t replace everything. Pick the problem you have today; Abstract swaps in one layer and the rest of your stack stays put.
What teams tell us
Swap one layer:
Security leaders in consumer, defence-tech and supply-chain intelligence, in their own words.
Also in productionAll customers ↗
Collect once from any source, then route each stream to the destination it’s worth paying for. Pick a source to trace its route.
Representative sources and destinations. See abstract.security for the full integration list.
We'd love for you to be part of the journey. Grab a coffee with us, have a chat and see a demo, from one of the most fun teams in cyber.