Security teams are drowning in telemetry. Endpoints, cloud platforms, identity providers, network sensors and applications all emit logs, metrics and events around the clock, and most of that data is forwarded straight into a SIEM that is priced by the gigabyte. The result is a bill that grows faster than the security value, and a data lake full of records nobody ever queries.
A Security Data Pipeline Platform (SDPP) sits between your telemetry sources and their destinations. Instead of shipping everything to one SIEM, an SDPP collects data once, then routes, filters, reduces, normalises and enriches it before deciding where each event should go. High value detections can flow to the SIEM, full fidelity copies can land cheaply in object storage, and noisy or duplicate data can be dropped or summarised. The pipeline also decouples your data from any single vendor, so adding or switching a SIEM stops being a rip-and-replace project.
This roundup covers the leading SDPP options on the market in 2026, from security-native platforms to general observability pipelines and open-source collectors. We lead with Realm.Security, which is featured on CyberSecTool, then survey the wider field so you can match an approach to your stack.
Realm.Security
Realm.Security positions itself as a security data fabric rather than a linear pipeline. The distinction matters. Where a classic pipeline moves data from A to B with some processing in between, a fabric treats your security telemetry as a connected layer that can be routed, shaped and served to many destinations at once, with the logic centralised rather than scattered across brittle forwarder configs.
The platform is built specifically for security data, not repurposed from general observability. It focuses on intelligent routing and optimisation of security telemetry: reducing volume before it reaches an ingest meter, normalising formats so downstream tools speak the same language, and sending the right slice of data to the right destination, whether that is a SIEM, an XDR, a data lake or long-term storage.
For teams whose Splunk or Microsoft Sentinel bill has become the largest line item in the security budget, that reduction and routing is the headline benefit. Just as important is the flexibility: because the fabric owns the collection and shaping layer, adding, testing or migrating a destination no longer means re-plumbing every source. Pricing is custom, which is typical for platforms sold on the volume of telemetry they process.
Realm.Security suits security engineering and detection teams who want to control cost and keep full fidelity data without being locked into a single analytics vendor. You can read the full Realm.Security profile for capabilities, integrations and deployment detail, or see where it sits in the wider category in our Security Data Pipeline guide.
Cribl
Cribl is the platform that defined this category. Cribl Stream popularised the idea of a vendor-neutral pipeline that routes, reduces and transforms machine data in flight, and it remains the reference point most buyers compare against. A large connector ecosystem, replay from cheap storage and mature in-flight processing make it a safe default for enterprises standardising their telemetry layer. See the Cribl profile for detail.
Observo AI
Observo AI applies machine learning to the pipeline itself, using AI to classify, deduplicate and optimise data so teams spend less time hand-tuning rules. It is one of a newer group of platforms betting that routing and reduction decisions are better made by models than by static config. See the Observo AI profile.
CeTu
CeTu is an AI-driven security data pipeline focused on intelligent log ingestion, aimed at cutting the volume that reaches the SIEM while preserving the signal analysts actually need. See the CeTu profile.
Sawmills
Sawmills is a newer AI telemetry pipeline that filters and optimises logs, metrics and traces at the point of collection. It targets the same cost and noise problem from the observability side as much as the security side. See the Sawmills profile.
Datadog Observability Pipelines
Datadog Observability Pipelines is a managed pipeline built on the open-source Vector project. If you already live in Datadog, it offers routing and transformation without standing up your own collector fleet, though its centre of gravity is observability rather than security specifically. See the Datadog Observability Pipelines profile.
Mezmo
Mezmo, formerly LogDNA, has moved from log management into telemetry pipelines, with intelligent processing to shape and reduce data before it reaches its destination. See the Mezmo profile.
Open-source options
If you would rather assemble your own layer, three open-source projects anchor the field: Vector, a high-performance pipeline written in Rust and stewarded by Datadog; Fluentd, the CNCF-graduated unified logging collector; and Tenzir, which is built specifically for security data pipelines and native security formats. These trade managed convenience for control and zero licence cost, at the price of running the infrastructure yourself.
How to choose an SDPP
The right platform depends less on feature checklists than on your stack and cost model. A few questions separate the options:
- Security-native or general observability. Realm.Security, Cribl, Observo AI, CeTu and Tenzir are built with security telemetry and formats in mind. Vector, Fluentd, Datadog and Mezmo come from the observability world and work for security with more assembly.
- How much volume it can actually remove. The business case rests on reduction, so ask for measured before-and-after volume on data like yours, not a headline percentage.
- Normalisation and OCSF. If you are standardising on the Open Cybersecurity Schema Framework, check how much of the mapping the platform does for you.
- Routing flexibility. Can it fan one stream out to a SIEM, a data lake and cold storage at once, and how hard is it to add a destination later.
- Cost model. Pipelines priced on data processed can themselves get expensive at scale, so model it against your current ingest bill.
- Deployment. SaaS, self-hosted or hybrid, and whether that fits your data residency and control needs.
Related reading
- Our full Security Data Pipeline guide, with every tool in the category
- Best Security Data Pipelines for a shortlist by use case
- Best open-source security data pipeline tools if you are building your own layer