Security data pipelines, by problem, capability, and sector.
Start from the problem in front of you. Every page shows the mechanism, the numbers behind it, and how to test it on your own data.
- Optimize
- Collect & transform
- Route & analyze
- Use cases
- By sector
- Compare Fleak
Optimize
The problems that put a data pipeline on the roadmap: SIEM cost, alert fatigue, token spend, parser toil, and drift.
- SIEM cost
Pay for signal, not noise
Your SIEM bill is a noise tax.
See detail → - Alert fatigue
Less noise in — better detections out
Alert fatigue isn't a volume problem.
See detail → - LLM token usage
Up to 40% token reduction for AI agents
Your AI agent is doing data engineering in its context window.
See detail → - Manual parsing
Any source, any schema — generated in 3 min
Your engineers are building parsers. They should be building product.
See detail → - Schema drift
Self-heals in-stream — zero manual intervention
Your pipelines didn't fail. They just went quietly wrong.
See detail →
Collect & transform
Get every source in, in one schema, with context attached and sensitive values masked.
- Collection
Every source, one ingestion layer
Receivers, API connectors, and cloud log discovery.
See detail → - Edge
Collect and filter at the source
One agent, managed centrally, cutting volume before it moves.
See detail → - OCSF
Every source in OCSF, long tail included
211 templates, plus AI-generated mappings for the rest.
See detail → - Enrichment
Context in every event
Identity, asset, threat intel, and geo context in flight.
See detail → - PII masking
Mask sensitive data in flight
Detectors, custom patterns, hashing, and quarantine.
See detail → - Pipeline lifecycle
Test, version, and roll back
Pipelines built and shipped like software.
See detail →
Route & analyze
Send each event where its job gets done, cut what nobody reads, and keep the rest a query away.
- Routing
Route every log by what it's for
SIEM, lake, and archive, each in its own schema.
See detail → - Log reduction
Less volume, same coverage
Filter, trim, deduplicate, sample, and aggregate in flight.
See detail → - Replay
Store it cheap, replay it when it matters
A full-fidelity archive, replayed through the same pipeline.
See detail → - Detection
Catch the known-bad before it's indexed
Sigma rules and ATT&CK coverage on normalized events.
See detail → - Investigations
Investigate without rehydrating
One search across the lake, the archive, and the SIEM.
See detail → - Monitoring
Know a pipeline broke before the dashboard does
Throughput, errors, field health, and alerts.
See detail →
Use cases
The programs security and platform teams run on Fleak, from SIEM migrations to AI agents.
- SIEM migration
Run both SIEMs, cut over by source
Every source dual-shipped from one pipeline.
See detail → - SIEM evaluation
Evaluate SIEMs on your own data
Every candidate fed the same live events.
See detail → - Security data lake
OCSF tables in your lake on arrival
Databricks, Snowflake, BigQuery, ClickHouse, and S3.
See detail → - Observability
Observability that scales with value
Logs to metrics, trace sampling, cost per team.
See detail → - AI agents & MCP
Pipelines in plain English, data over MCP
A copilot for engineers. An MCP server for agents.
See detail → - AI observability
Every agent session on the record
Normalize, mask, and route AI agent telemetry.
See detail →
By sector
The same fabric, tuned to the systems and constraints of each industry.
- Cybersecurity
Not every log deserves a SIEM
Every source normalized, every log routed by intent.
See detail → - Financial services
Regulated data, normalized inside your perimeter
Security, transaction, and regulatory data in one schema.
See detail → - Healthcare
Healthcare data, protected before it moves
PHI masked in flight, EHR and device telemetry normalized.
See detail → - Public sector
Mandate-ready logging at storage prices
M-21-31 retention, zero trust telemetry, your boundary.
See detail → - Industrial / OT
Every PLC, historian, and sensor, fused at the edge
OT telemetry routed to where the work happens.
See detail → - Aviation & logistics
Two hundred pipelines, one fabric
The iPaaS estate retired, on your own infrastructure.
See detail → - Retail
Every store, site, and supply chain in one schema
Card data masked, store data filtered at the edge.
See detail → - Technology
Telemetry as a shared service, not a tax
Cloud, Kubernetes, and AI product telemetry for every team.
See detail → - Telecom & media
Network and subscriber data at carrier scale
Flow data normalized, subscriber data masked in flight.
See detail → - Vendors & MSSPs
Ship every integration your customers ask for
Embedded connectors, generated parsers, OCSF output.
See detail →
Compare Fleak
How Fleak compares with other security data pipelines, starting with what each does well.
- Comparison
Fleak vs Cribl
The routing incumbent vs an AI-native normalization layer. Run them together or switch.
See detail → - Comparison
Fleak vs Monad
Prebuilt connector templates vs mappings generated for any source, long tail included.
See detail → - Comparison
Fleak vs DataBahn
Autonomous agents on data in motion vs AI in the control plane and a deterministic engine.
See detail → - Comparison
Fleak vs Observo AI
SentinelOne's bundled pipeline vs a neutral layer for every SIEM and lake.
See detail → - Comparison
Fleak vs Bindplane
OpenTelemetry collector management vs security normalization, routing, and repair.
See detail →
Bring your noisiest log source.
30 minutes. We connect it live, normalize it, and show where each event type belongs.