AWS Security Lake Preview — Architecture, Governance, and Operations
AWS Security Lake centralizes multi-account security telemetry using the Open Cybersecurity Schema Framework, requiring governance, access controls, and outcome metrics to fuel analytics and incident response at scale.
Executive briefing: At AWS re:Invent on , Amazon Web Services announced AWS Security Lake, a service that automatically centralizes security telemetry from AWS accounts, on-premises sources, and third-party SaaS products using the Open Cybersecurity Schema Framework (OCSF). Security Lake builds data lakes on Amazon S3, curates data into optimized formats in Amazon Athena and Amazon OpenSearch, and integrates with partner analytics tools. The service entered preview in select regions, offering security teams a managed pipeline for collecting logs, alerts, and findings at petabyte scale.
Architecture and data flow
Security Lake uses AWS Lake Formation, AWS Glue, and AWS Lambda to ingest and transform data. Customers designate a delegated administrator account that configures data sources—CloudTrail, VPC Flow Logs, Route 53 Resolver Query Logs, AWS Security Hub, GuardDuty, and custom logs. Data is converted into OCSF-compliant Parquet files partitioned by time and AWS Region, stored in Amazon S3 buckets with Lake Formation governance. AWS Glue Data Catalog registers tables, enabling queries via Amazon Athena, Amazon SageMaker, and partner SIEM/XDR tools.
The service supports integration with partner sources such as CrowdStrike, Okta, Palo Alto Networks, and IBM Security QRadar. Customers can ingest on-premises logs via Amazon Kinesis Data Firehose or Amazon Simple Storage Service (S3) batch imports. Security Lake also exposes APIs for third-party tools to subscribe to data and for customers to build custom analytics pipelines.
Governance and access control
Security Lake leverages Lake Formation permissions to control access at the table, column, or row level. Organizations can use AWS Organizations to designate delegated administrators and apply policies across accounts. Fine-grained access controls allow different teams (SOC analysts, threat hunters, compliance auditors) to query relevant datasets without overexposing sensitive data. Data residency is managed per region, with replication options for resilience.
Data lifecycle policies support retention management, enabling tiering to Amazon S3 Glacier or deletion based on compliance requirements (for example, PCI DSS, HIPAA). Customers should document retention schedules aligned with regulatory obligations and security operations needs.
Operational benefits
Security Lake addresses longstanding challenges with security data silos. Benefits include:
- Standardization: OCSF normalization reduces the need for custom parsers and accelerates correlation across log types.
- Cost efficiency: Data lake storage on Amazon S3 offers lower cost compared to traditional SIEM ingestion, while query-on-demand services reduce infrastructure overhead.
- Scalability: Managed ingestion pipelines handle high-volume logs without manual ETL maintenance.
- Analytics flexibility: Integration with Athena, SageMaker, and partner tools supports threat hunting, machine learning, and compliance reporting.
Implementation roadmap
Security teams adopting Security Lake should follow a structured approach:
- Assessment: Inventory existing AWS accounts, logging configurations, and third-party integrations. Identify compliance requirements dictating data retention, residency, and access controls.
- Design: Define delegated administrator accounts, governance model, and access roles. Plan OCSF mapping for custom logs and determine integration with existing SIEM or data lake platforms.
- Pilot: Enable Security Lake in non-production accounts, ingest core AWS logs, and validate OCSF schema mapping. Test queries with Athena, validate data quality, and benchmark performance.
- Scale: Expand ingestion to additional accounts, regions, and partner sources. Integrate with threat detection platforms, incident response workflows, and data science pipelines.
- Operate: Establish monitoring, cost management, and lifecycle policies. Document runbooks for onboarding new data sources and responding to incidents.
Integration with security operations
Security Lake can feed detection and response processes. SOC teams can build Athena queries or use Amazon Security Analytics partners (for example, Splunk, Sumo Logic, Exabeam) to detect anomalies. Incident response teams can correlate GuardDuty findings with VPC flow logs and CloudTrail events stored in the lake. Threat hunters can run SageMaker notebooks to analyze OCSF data, build machine learning models for anomaly detection, and push results back to Security Hub.
Integration with AWS Security Hub allows consolidated findings with Security Lake context. Customers can use EventBridge to trigger automated responses (for example, isolating EC2 instances) based on analytics results. Security Lake also supports sharing data across AWS accounts for centralized operations or managed security service providers.
Outcome metrics and monitoring
To measure success, organizations should track:
- Coverage: Percentage of critical log sources onboarded to Security Lake.
- Query performance: Average query latency and cost per TB processed in Athena or partner tools.
- Detection efficiency: Time to detect and investigate incidents using Security Lake data compared to prior SIEM workflows.
- Data quality: Number of schema validation errors, missing partitions, or ingestion failures.
- Cost optimization: Storage and query cost trends, use of S3 lifecycle policies, and reserved capacity savings.
Dashboards should surface these metrics to security leadership and FinOps teams. Regular reviews should align ingestion scope with evolving threat landscapes and compliance requirements.
Security and compliance considerations
Security Lake integrates with AWS security services: KMS for encryption at rest, CloudTrail for audit logs, and AWS IAM for authentication. Customers should enable server-side encryption, enforce TLS, and configure CloudTrail data events for Security Lake buckets. Access should use federated identities (AWS IAM Identity Center) with MFA. For compliance, document data flows, retention, and access reviews to satisfy standards like ISO/IEC 27001, SOC 2, HIPAA, and GDPR.
Customers in regulated industries should validate that OCSF normalization preserves required data fields for regulatory reporting. Data localization requirements may necessitate region-specific deployments. Implement tagging and data classification to manage sensitive information.
Third-party ecosystem
AWS launched a partner program for Security Lake, with vendors providing connectors, analytics, and managed services. Evaluating partners requires due diligence on security practices, SLAs, and cost models. Consider integration with AWS Marketplace private offers and consolidated billing. Organizations can also build custom ETL pipelines using AWS Glue or EMR for specialized analytics.
Continuous improvement
As Security Lake evolves (general availability launched in 2023), customers should monitor roadmap updates—additional data sources, region expansion, automated data quality checks, and deeper integration with AWS security services. Participation in AWS re:Invent sessions, AWS Summits, and AWS Security Hub communities can provide best practices. Establish feedback loops with application teams to onboard new logs and improve visibility as workloads modernize.
Zeph Tech’s cloud security engineering group piloted AWS Security Lake across regulated workloads, mapping custom application logs to OCSF, integrating Athena threat hunting queries with incident response playbooks, and tracking coverage and cost metrics through unified dashboards.
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