
Open source cloud-native SIEM built on a security data lake for AWS
Matano is an open source cloud-native security data lake platform and SIEM alternative built for AWS. It normalizes unstructured security logs into a structured real-time data lake, enabling petabyte-scale threat hunting, detection, and response with serverless architecture and zero vendor lock-in.
Normalize unstructured security logs into a structured real-time data lake in your own AWS account using Apache Iceberg and ECS standards
Out-of-the-box correlation rules tuned to your environment for real-time threat detection and remediation
Build real-time detections using Python with support for automatic import of Sigma detection rules
Hundreds of prebuilt integrations and parsers for automatic security data ingestion from all major security products
Intuitive search language compatible with Splunk SPL for searching data and building detection rules across the data lake
Custom VRL (Vector Remap Language) scripting to parse, enrich, normalize, and transform logs during ingestion without managing servers
Lowest storage costs possible using S3 object storage with unlimited data retention
Replace legacy SIEM solutions like Splunk or Elastic with a cloud-native data lake platform that eliminates cost and scalability limitations
Monitor and detect threats across your entire AWS environment with deep integrations spanning data, infrastructure, network, and IAM
Hunt for threats across petabytes of security data with low-cost retention and fast search using Splunk-compatible query language
Complement an existing SIEM by offloading high-volume log storage and analytics to a cost-effective security data lake
Deep integrations across AWS ecosystem spanning data, infrastructure, network, and IAM for cloud detection and response at scale
Zero-ops serverless design on AWS that scales elastically without infrastructure setup or maintenance
Detect and respond to complex API attacks and fraud by building an API data lake with subsecond search capabilities

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