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Most organizations run infrastructure across multiple places — on-premises data centers, many AWS accounts and Regions, and other cloud providers (Azure, GCP). Each platform and application generates telemetry and logs that often end up scattered across different services and storage locations. That dispersion makes investigation, threat detection, and compliance harder because security teams must hunt across many environments. AWS Security Lake centralizes security-relevant logs and events from multiple sources into a single, queryable data lake so you can perform cross-account searches, analytics, and long-term retention with familiar AWS tooling.
A slide titled "Security Lake" showing a simple architecture diagram: a central orange node (developer/ingest) with arrows going up to three targets labeled Data Center, Account 1 (AWS), and Account 2 (AWS).
What Security Lake gives you
  • Central ingestion: Collect telemetry from AWS services, external clouds, on-premises sources, and supported third-party integrations.
  • Normalized, queryable data: Converts and catalogs logs into efficient formats for analytics.
  • Managed S3 storage: Security Lake provisions and organizes Amazon S3 buckets so you can apply encryption, lifecycle policies, and IAM controls.
  • Analytics-ready output: Query data using Amazon Athena (SQL) or integrate with SIEMs and analytics tools that read from S3.
How Security Lake works — overview
  1. Collect: Ingests telemetry such as AWS CloudTrail, VPC Flow Logs, DNS logs, host telemetry, and other security-relevant events from multiple accounts, Regions, and external sources.
  2. Store: Writes incoming data into S3 buckets that Security Lake provisions and manages so you can leverage S3 features (encryption, lifecycle rules, access control).
  3. Normalize: Partitions data, converts logs into Parquet for efficient storage and queries, and can map event schemas to the Open Cybersecurity Schema Framework (OCSF) for uniform fields across sources.
  4. Access & analyze: After normalization and cataloging, query with Amazon Athena for ad-hoc SQL analysis or integrate outputs with SIEMs and analytics pipelines.
A flow diagram titled "Security Lake Flow" showing steps: collect logs, store them in S3 buckets, normalize AWS logs into OCSF, and then provide data access and query access. The steps are represented by colored circular icons connected with arrows.
Common data sources and benefits Key Security Lake features
A presentation slide titled "Features" showing five numbered feature cards with colorful icons. The cards list: Data Aggregation; Variety of Supported Log and Event Sources; Data Transformation and Normalization; Multi-Account and Multi-Region Data Management; and Data Lifecycle Management and Optimization.
Operational and security notes
Security Lake provisions S3 buckets and data catalogs for you, but you retain control via IAM policies, S3 bucket policies, and encryption settings. Plan access controls and lifecycle rules before onboarding large volumes of telemetry to avoid unexpected costs.
Ensure least-privilege IAM and proper encryption when sharing Security Lake outputs with analytics tools or external teams. Sensitive telemetry can contain PII or security-critical details — treat access and retention carefully.
When to use Security Lake
  • You need centralized, cross-account visibility for investigations and compliance.
  • You want normalized schemas (OCSF) to simplify cross-source analytics.
  • You prefer managed ingestion and storage while using familiar AWS tools (S3, Athena) for querying.
  • You want to integrate Security Lake outputs with SIEMs and third-party analytics.
Links and references

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