Balázs Scheidler - Axofllow
by 
Balázs Scheidler
July 23, 2026

Closing the Data-Detection Gap: What We're Building

SANS 2026: 80% of detection teams can barely keep pace. Faster rule-writing won't help, a stable data layer will. See how to run Sigma in-stream, before the SIEM
SANS 2026: 80% of detection teams can barely keep pace. Faster rule-writing won't help, a stable data layer will. See how to run Sigma in-stream, before the SIEM
Bence Csáti - Axoflow
by 
Bence Csáti
July 20, 2026

Raw Logs Get AI Triage Wrong. Every Time.

We gave a security agent raw, unnormalized vendor logs and asked it to triage a real attack scenario. Nine runs, zero correct. Here's why raw logs break AI triage — and what changes once the data is normalized and enriched.
Raw Logs Get AI Triage Wrong. Every Time.

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Long-Term Log Storage Without SIEM Costs: The Axoflow Storage Layer
Peter Andi
by 
Peter Andi
June 25, 2026

Long-Term Log Storage Without SIEM Costs: The Axoflow Storage Layer

Store security logs long-term without SIEM costs or vendor lock-in. See how the Axoflow Storage Layer uses open Parquet files on object storage you control.
Security Data Pipeline Platform comparison 2026 — seven platforms evaluated across on-premises lake and deterministic normalization criteria
Máté Benedek - Axoflow
by 
Mate Benedek
June 16, 2026

Security Data Pipeline Platform (SDPP) Comparison 2026: What the Seven-Platform Reviews Miss

What the seven-platform SDPP comparisons miss: two criteria that change the answer for enterprise security teams evaluating pipelines in 2026-2027.
The pipeline was never the destination - Axoflow blog
Balázs Scheidler - Axofllow
by 
Balázs Scheidler
June 15, 2026

The pipeline was never the destination

Gartner's 2026 Hype Cycle dropped standalone security data pipelines and added Security Data Lakes. What this market shift means for your SOC.
How Federal Agencies can meet the requirements of the OMB M-26-14 logging mandate, and how Axoflow can help them to get there fast and in a cost-effective way
Balázs Scheidler - Axofllow
by 
Balázs Scheidler
June 11, 2026

OMB M-26-14: What Federal Agencies Need to Know About the New Logging Mandate

How Federal Agencies can meet the requirements of the OMB M-26-14 logging mandate, and how Axoflow can help them to get there fast and in a cost-effective way
How AxoSyslog implements in-application ACK, flow control, memory and disk buffers, batching, and how log-iw-size interacts with batch-lines under the hood
Attila Szakács - Axoflow
by 
Attila Szakács
June 4, 2026

AxoSyslog internals: flow control, window size, queues, and batching

How AxoSyslog implements in-application ACK, flow control, memory and disk buffers, batching, and how log-iw-size interacts with batch-lines under the hood
The Data Floor Sets the AI Ceiling
Balázs Scheidler - Axofllow
by 
Balázs Scheidler
May 28, 2026

The Data Floor Sets the AI Ceiling

AI SOC performance is capped by telemetry quality. Learn why vendor-owned normalization, not brittle rule maintenance, sets your AI ceiling.
Why Detection Engineering Can't Work Without a Pipeline That Keeps Up
Sándor Guba - Axoflow
by 
Sándor Guba
May 6, 2026

No More Parser Maintenance: Why Detection Engineering Can't Work Without a Pipeline That Keeps Up

Detection rules fail silently when parsers miss schema changes. Learn how autonomous classification keeps your fields where your detections expect them.
How delivery guarantees, flow control, and buffers behave in security data pipelines across UDP, TCP, HTTP, gRPC, OpenTelemetry Collector, Kafka, and AxoSyslog.
Attila Szakács - Axoflow
by 
Attila Szakács
April 28, 2026

Message delivery guarantees in security data pipelines

How delivery guarantees, flow control, and buffers behave in security data pipelines across UDP, TCP, HTTP, gRPC, OpenTelemetry Collector, Kafka, and AxoSyslog.
Why Your AI SOC Is Only as Good as the Data Feeding It
james-luby
by 
James Luby
April 24, 2026

Why Your AI SOC Is Only as Good as the Data Feeding It

AI SOCs underperform when fed messy, human-oriented telemetry. Why machine-readable, normalized security data is the real foundation of an AI-native SOC.

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