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PROBLEMS OF TODAY’S SIEM “ECOSYSTEM”
Vendor lock-in holds teams hostage
Security teams are trapped in someone else's roadmap. Every "upgrade" is either a rip-and-replace or a costly bill for overlapping features.
Stuck in the data “swamp”
All data gets treated the same—indexed, stored, priced identically—whether it's critical threat telemetry or compliance logs you'll never query. Storage costs spiral. You're either broke or blind.
The Economics Are Broken
Modern SIEM pricing scales with data volume. Security data grows 25–30% annually. Cloud-native environments multiply log sources. Compliance forces long retention. Most stored data is rarely queried.
THE Architecture Is Coupled
Legacy SIEMs were built as monoliths where everything is tightly bound.That creates three downstream issues:You can’t upgrade detection without touching storage. You can’t change storage without replatforming. You can’t route data intelligently before it hits the billing meter. Detection becomes constrained by indexing models designed 10–15 years ago.
In a multi-cloud, streaming world, that coupling is a liability.
Abstract is Composable SIEM, a flexible, composable platform where each function can be swapped, scaled, or integrated. A platform that evolves at the speed of SecOps.
Collection is no longer just log ingestion. In a composable SIEM, it becomes a security data fabric, the control point where data is shaped before it becomes expensive, rigid, or locked into a single system.
- Collect from any source: cloud, SaaS, network, endpoint, identity
- Normalize into common schemas or multiple schemas.
- Enrich with asset, identity, environment, and threat context.
- Shape, filter, and route data based on analytical intent.
Detection needs data, and it needs reliable, consistent data, but it does not require that all data be centralized, indexed, or treated equally. Modern security operations rely on multiple detection models, each optimized for a different purpose and time horizon:
- In-stream detection for real-time and near-real-time threats, where speed and precision are critical.
- Historical detection for low and slow, retroactive analysis, investigations, and hypothesis testing.
- Distributed detection across multiple environments, so analytics run where your data lives without forcing everything into a single pipeline.
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Not all security data has the same value, the same urgency, or the same lifecycle. A modern SIEM state of mind requires deliberately placing the right data into the right storage, in the right locations, for the right use cases.
Abstract enables that through three tiers:
- Real Time Storage for instantaneous retrieval, where milliseconds make the difference.
- Hot Storage for high-value, high-signal data that detection engines can access quickly.
- Warm Storage for cost-efficient retention that meets regulatory and compliance purposes.
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Security operations is where SIEM becomes real, and where AI must be deeply embedded, not bolted on. In a composable SIEM, SOC workflows operate as an AI-enabled control plane across the entire architecture, independent of where data lives or which engine produced the alert. This includes:
- AI-assisted triage and prioritization
- Context-rich investigations
- Proactive threat hunting
- A unified analyst workbench
- Response and SOAR workflows, all AI-enabled
Start where your stack
NEEDS THE MOST HELP
Different teams start in different places. Abstract layers into your existing stack so you can improve what matters most without replacing everything.
"We don’t have a strategy. We have a dependency"
Security leaders can’t swap components without ripping everything out. Detection tied to storage. Storage tied to licensing.
Licensing tied to vendor ecosystem.
Multi-cloud visibility fragmented.
"We bought AI. We're still drowning."
AI layered on top of the same noisy, context-free data.
Summaries of alerts that shouldn't exist in the first place.
The volume is overwhelming.
The signal is still buried.
A better flashlight doesn't fix the wiring.
"Our SIEM bill grows every year. Our confidence in it doesn't."
Pricing scales with data volume, not with data value.
80% of stored data never used for detection or response.
Hot storage prices paid for compliance-only data.
No real visibility into whether a genuine attack would be caught.
"We’re drowning in alerts but still worried we’re missing something real."
Detection engines are overloaded with low-value logs.
Most detections still rely on rules written years ago.
Streaming detections are limited; everything waits for indexing.
AI is layered on top of noisy alerts. False positives overwhelm analysts.
"We’re paying to collect data we don’t even use, and we can’t shape it before it hits the meter."
Data onboarding is slow, brittle, and vendor-controlled.
Every new source requires professional services.
Normalization breaks when schemas change.
Routing data to multiple tools doubles ingestion cost.
Cloud-native logs (VPC, SaaS, identity) explode volume overnight.
ABSTRACTED
We’d love for you to be part of the journey. Let’s grab a coffee, have a chat, and set up a demo!
Your friends at Abstract — one of the most fun teams in cyber.
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