Security data isn’t just plumbing, it needs intelligence. Bolting on generic pipeline doesn’t solve the deeper issue and risks becoming a liability, not an improvement.
Key Risks of Generic, Bolt-On Pipelines
- Integration Is Not Automatically Solved
- Many pipelines lack built-in security parsers for the wide variety of sources in an enterprise environment.
- Without native support, security teams often take on the heavy lifting — building and maintaining custom parsers themselves.
- Security Context Is Missing
- Generic pipelines make decisions based on superficial attributes (e.g., log volume or source), not on whether a particular event is important for threat detection.
- That means critical security signals can be dropped before they ever reach your SIEM or analytics layer.
- Vendor Lock-In Concerns
- Some “new” pipeline solutions steer data toward a vendor’s preferred storage — limiting flexibility.
- Rather than giving you control over where data lives (your lake of choice, your analytics stack), you might get stuck in a silo.
What Should Security-Native Pipelines Do
A truly effective data pipeline for security must be built with security intelligence at its core:
- Enrich and filter data based on threat context – not just generic metadata.
- Normalize events intelligently, so they align with detection and analytics needs.
- Route data in a way that supports both real-time detection (high-fidelity data) and compliance or investigation (full-fidelity data).
- Support data sovereignty: it should work with the data platforms (e.g. Snowflake, Databricks, cloud lakes) you’ve already chosen – not force you into a vendor lock-in.
The Call to Action: Demand More Than Just a Pipeline
- “Do you offer native parsers or only shift the burden to our team?”
- “Is the filtering logic threat-aware, or oblivious to true detection value?”
- “Can we choose where our data lands, or are we locked into your storage stack?”
If the answer doesn’t include security-native intelligence, you’re not solving the real problem – just masking it.
Bottom Line
The wave of data security pipelines isn’t bad — it’s overdue. But it’s only the first step. To truly optimize security data, teams need smart pipelines, not just bigger ones. A security-native data optimizer doesn’t just shrink your data; it makes your data smarter. That’s the kind of architecture that improves detection, reduces risk, and gives you control – all at once.