Don’t worry — we’ll explain all this jargon in a second.
SIEM stands for “Security Information and Event Management” and refers to log management tools used by security teams to detect threats from an organization's security logs (network, host, cloud, SaaS audit logs, etc.) and send alerts about them. Security engineers write detection rules inside the SIEM as queries to detect suspicious activity and create alerts. For example, a security engineer could write a detection rule that checks the fields in each CloudTrail log and creates an alert whenever an S3 bucket is modified with public access, to prevent data exfiltration.
Traditional SIEM tools (e.g. Splunk, Elastic) used to analyze security data are difficult to manage for security teams on the cloud. Most don’t scale because they are built on top of a NoSQL database or search engine like Elasticsearch. And they are expensive — the enterprise SIEM vendors have costly ingest-based licenses. Since security data from SaaS and cloud environments can exceed hundreds of terabytes, teams are left with unsatisfactory options: either not collect some data, leave some data unprocessed, pay exorbitant fees to an enterprise vendor, or build their own large-scale solution for data storage (aka “data lake”).
Companies like Apple, HSBC, and Brex do the latter: they build their own security data lakes to analyze their security data without breaking the bank. “Data lake” is jargon for heterogeneous data that is too large to be kept in a standard database and is analyzed directly from object storage like S3. A “security data lake” is a repository of security logs parsed and normalized into a common structure and stored in object storage for cost-effective analysis. Building your own data lake is a fine option if you’re big enough to justify the cost — but most companies can’t afford it.
Then there’s the vendor lock-in issue. SIEM vendors store data in proprietary formats that make it difficult to use outside of their ecosystem. Even with "next-gen" products that leverage data lake technology, it's nearly impossible to swap out your data analytics stack or migrate your security data to another tool because of a tight coupling of systems designed to keep you locked in.
Security programs also suffer because of poor data quality. Most SIEMs today are built as search engines or databases that query unstructured/semi-structured logs. This requires you to heavily index data upfront which is inefficient, expensive and makes it hard to analyze months of data. Writing detection rules requires analysts to use vendor-specific DSLs that lack the flexibility to model complex attacker behaviors. Without structured and normalized data, it is difficult to correlate across data sources and build effective rules that don’t create many false positive alerts.
While the cybersecurity industry has been stuck dealing with these legacy architectures, the data analytics industry has seen a ton of innovation through open-source initiatives such as Apache Iceberg, Parquet, and Arrow, delivering massive cost savings and performance breakthroughs.
We encountered this problem when building out petabyte-scale data platforms at Amazon and Duo Security. We realized that most security teams don't have the resources to build a security data lake in-house or take advantage of modern analytics tools, so they’re stuck with legacy SIEM tools that predate the cloud.
We quit our jobs at AWS and started Matano to close the gap between these two worlds by building an OSS platform that helps security teams leverage the modern data stack (e.g. Spark, Athena, Snowflake) and efficiently analyze security data from all the disparate sources across an organization.
Matano lets you ingest petabytes of security and log data from various sources, store and query them in an open data lake, and create Python detections as code for realtime alerting.
Matano works by normalizing 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 and analysis at petabyte scale. To prevent vendor lock-in, Matano uses Apache Iceberg, a new open table format that lets you bring your own analytics stack (Athena, Snowflake, Spark, etc.) and query your data from different tools without having to copy any data. By normalizing fields according to the Elastic Common Schema (ECS), we help you easily search for indicators across your data lake, pivot on common fields, and write detection rules that are agnostic to vendor formats.
We support native integrations to pull security logs from popular SaaS, Cloud, Host, and Network sources and custom JSON/CSV/Text log sources. Matano includes a built-in log transformation pipeline that lets you easily parse and transform logs at ingest time using Vector Remap Language (VRL) without needing additional tools (e.g. Logstash, Cribl).
Matano uses a detection-as-code approach which lets you use Python to implement realtime alerting on your log data, and lets you use standard dev practices by managing rules in Git (test, code review, audit). Advanced detections that correlate across events and alerts can be written using SQL and executed on a scheduled basis.
We built Matano to be fully serverless using technologies like Lambda, S3, and SQS for elastic horizontal scaling. We use Rust and Apache Arrow for high performance. Matano works well with your existing data stack, allowing you to plug in tools like Tableau, Grafana, Metabase, or Quicksight for visualization and use query engines like Snowflake, Athena, or Trino for analysis.
Matano is free and open source software licensed under the Apache-2.0 license. Our use of open table and common schema standards gives you full ownership of your security data in a vendor neutral format. We plan on monetizing by offering a cloud product that includes enterprise and collaborative features to be able to use Matano as a complete replacement to SIEM.
We’d love to hear about your experiences with SIEM, security data tooling, and anything you’d like to share!