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Matano

Y Combinator LogoW23
Active
devsecops
analytics
open-source
cybersecurity
Seattle
 
https://www.matano.dev

Cloud Native SIEM (alternative to Splunk/Elastic)

Matano is a modern SIEM, built for cloud-first security teams. It replaces traditional SIEM databases like Splunk or Elastic with a cybersecurity platform built on top of a cost-effective Security Data Lake.
Matano
Founded:2022
Team Size:2
Location:Seattle, WA
Group Partner:Diana Hu
    

Active Founders

Samrose Ahmed, Founder

Hi I'm Samrose, a software engineer by background. I grew up in NYC, went to CMU, and worked at AWS for a few years before starting Matano. I'm especially interested in big data, cloud, and highly available systems. Outside of work, I'm an avid reader.
Samrose Ahmed
Samrose Ahmed
Matano
  

Shaeq Ahmed, Founder

Hey! Shaeq is a software engineer who originally hails from New York and loves all things open source, security and big data. He quit his job crunching data for autonomous robots at Amazon to build Matano - an open-source SIEM alternative for security teams on AWS.
Shaeq Ahmed
Shaeq Ahmed
Matano
 

Company Launches

Matano - Open source SIEM alternative for AWS

👋 Hi YC! We’re Samrose & Shaeq and we are excited to launch Matano.

TL;DR

Matano is a high-scale, low-cost, zero-ops alternative to traditional SIEM (e.g. Splunk) that deploys a security data lake directly in your AWS account. It lets you ingest petabytes of security and log data from various sources, store and query them in an open Apache Iceberg data lake, and create Python detections as code for realtime alerting. Matano is fully serverless and designed specifically for AWS.

Problem

  • Traditional tools used to analyze security data (SIEMs) don’t scale, and are too expensive and difficult to manage for security teams on the cloud.
  • Security data is now a Big Data problem: collecting data from your network, SaaS, and cloud environments can exceed 100TBs of data. Security teams are forced to either not collect some data, leave data unprocessed, or build an in-house data lake to cost-effectively analyze large datasets.
  • At scale, without a strategy to normalize data into a structured format, it is difficult to correlate across data sources & build effective alerts that don’t create many false positives. Traditional SIEM query-based rules fail to accurately identify threats.

Our Solution

We’re leveraging our years of experience at AWS in Big Data and cloud technologies to build a modern alternative to SIEM designed from the ground up for efficiency, scalability, cost effectivity, and vendor neutrality.

  • Security Data Lake Matano normalizes unstructured security logs into a structured realtime data lake in your AWS account. All data is stored in optimized Parquet files in S3 object storage for cost-effective retention & analysis at petabyte scale.
  • No vendor lock-in Matano lets you truly own your own security data. Cybersecurity vendors lock your data in proprietary formats which make it very difficult to use outside of their product. With Matano, all your data is in open Apache Iceberg tables that can can be directly queried from different tools (AWS Athena, Snowflake, etc.) without having to copy any data.
  • Detection-as-code Matano gives you the flexibility of Python to implement realtime alerting on your log data and introduce a development lifecycle to detection by managing rules in Git (test, code review, audit).
  • Serverless Matano is a fully serverless platform built entirely on technologies like Lambda, S3, and SQS and is designed for zero-ops and unlimited elastic horizontal scaling. Matano uses Rust for blazing fast performance 🦀.
  • Open source Matano is licensed as Apache 2.0 and always will be. Matano uses an open table format (Apache Iceberg) and common schemas (Elastic Common Schema), to give you full ownership of your security data.

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