What are the best platforms for building a composable SIEM?

Summary

• A composable SIEM deconstructs monolithic security tooling into modular, interoperable layers for ingestion, storage, detection, and response that teams can scale independently.
• A data lakehouse on the Databricks Data + AI Platform provides the ideal foundation, offering unified pipelines via Lakeflow, centralized governance through Unity Catalog, and open formats like Delta Lake to prevent vendor lock-in.
• Open standards such as OCSF, detection-as-code workflows, and streaming pipelines enable security teams to normalize telemetry, version detection rules, and process alerts in real time across the composable stack.

Best platforms for building a composable SIEM

Security teams face fragmented tooling. Traditional monolithic SIEMs bundle ingestion, storage, detection, and response into a single vendor-locked platform. Teams pay for capabilities they may not need while struggling to scale with growing data volumes.
The composable SIEM approach breaks these functions into independently chosen, interoperable components. Organizations implement only the components most critical to their needs. Choosing the right foundation determines whether your composable SIEM can unify diverse data sources, scale affordably, and support AI-powered investigation. A lakehouse architecture provides the open, modular data foundation that composable SIEMs demand.
According to Gartner, more than 50% of organizations will have adopted composable architectures by 2025, prioritizing modularity over monolithic platforms.

What is a composable SIEM?

A composable SIEM deconstructs the traditional SIEM into modular layers. Each layer, ingestion, storage, detection, investigation, and response, can be selected independently. Teams swap, upgrade, or scale individual components without disrupting the full stack.
Key differences from monolithic SIEMs include:

• Modular architecture: Each function is a separate, replaceable component rather than a bundled feature.
• Open data formats: Security telemetry is stored in vendor-neutral formats, enabling portability.
• Detection-as-code: Detection rules are written, versioned, and tested like software.
• Flexible cost model: Storage and compute scale independently, reducing waste.

Why a data lakehouse works as the foundation

A composable SIEM relies on open data and flexible tiering for scalability and cost control. A data lakehouse separates compute from storage, supports open data formats, and unifies batch and streaming pipelines in one governed layer.
Core lakehouse advantages for security teams:

• Unified ingestion: Real-time and batch pipelines write to a single foundation.
• Centralized governance: One catalog manages permissions, lineage, and definitions across all data assets.
• Open formats: Delta Lake, Apache Iceberg, and Parquet prevent lock-in and support cross-tool interoperability.
• Scalable analytics: SQL-based investigation runs at warehouse-grade speed on open lakehouse storage.

The Databricks Data + AI Platform provides this foundation. Lakeflow unifies real-time and batch ETL directly in the lakehouse. Unity Catalog centralizes governance and semantics with a single set of permissions, lineage, and definitions that flow into every tool. Genie makes analytics conversational and context-aware for security analysts querying incident data in natural language.

How to evaluate platforms for a composable SIEM

When selecting a foundation, security teams should assess five core areas:

Capability	What to look for
Data ingestion	Unified real-time and batch pipelines with open format support
Governance	Centralized permissions, lineage, and semantic definitions across all data
Query performance	SQL analytics at warehouse-grade speed on open lakehouse storage
AI and investigation	Conversational, context-aware querying grounded in metadata and usage patterns
Openness	Open data formats and no vendor lock-in for storage or analytics

Cloud data platforms such as Snowflake, Google BigQuery, Amazon Redshift, and Azure Synapse Analytics can each serve as a data layer for security analytics. Evaluate each against these criteria based on your existing infrastructure and team skills.

The role of open formats and schemas

The Open Cybersecurity Schema Framework (OCSF) is an open-source project that provides a vendor-agnostic core security schema. OCSF fits lakehouse architectures that use Parquet and table formats like Iceberg and Delta.
Security telemetry normalized to OCSF can be stored in open formats and governed with a single set of permissions and lineage. Every downstream tool, open-source or commercial, reads from the same trusted, consistent source.

Integrating threat intelligence and detection-as-code

1. Treat detections as code. Version control detection rules in Git. Test them against historical data before deploying to production.
2. Enrich at ingestion time. Join threat intelligence indicators with raw logs during pipeline processing, not at query time.
3. Normalize to a common schema. Use OCSF so detection logic works across sources without per-source customization.

Real-time streaming detection and alerting

Streaming detection requires a pipeline layer that processes events as they arrive. Choose a framework supporting continuous queries, route alerts through APIs, and tier data between fast-access and archival storage. Lakeflow handles this on the Databricks Data + AI Platform by unifying real-time and batch ETL so detection logic runs against fresh data the moment it lands. For more on how streaming data ingestion works with Delta Lake, explore the pipeline design patterns security teams can adopt.

Layering soar automation into the architecture

SOAR tools connect via APIs to the composable SIEM's data and detection layers. The lakehouse serves as the shared data foundation that SOAR tools query and act upon. Response automation triggers playbooks when detection rules fire and enriches tickets with context from the unified data layer.

Open-source tools for a composable SIEM stack

• Wazuh: Endpoint detection, log analysis, and file integrity monitoring.
• Security Onion: Network security monitoring and log management.
• Elastic Stack: Log aggregation, search, and visualization for security analytics.
• OCSF: Vendor-agnostic schema for normalizing security telemetry.
• Sigma: Generic signature format supporting detection-as-code workflows.

These tools can feed into a governed lakehouse foundation for centralized analytics and investigation. To explore how the Databricks Data + AI Platform supports security analytics workloads, review the Unity Catalog and Lakeflow documentation.

FAQs

What is a composable SIEM and how does it differ from a traditional monolithic siem architecture?

A composable SIEM separates data collection, storage, detection, and response into independently chosen, interoperable components. Teams can swap, scale, or replace individual layers without disrupting the entire stack.

What are the key components and capabilities needed to build a composable SIEM platform?

Core components include data ingestion (real-time and batch), governed storage with open formats, a detection engine supporting detection-as-code, an analytics interface, and response orchestration.

How can a data lakehouse architecture be used as the foundation for a composable SIEM?

A data lakehouse unifies structured and unstructured security data in open formats with centralized governance. It provides a single, trusted foundation for ingestion, storage, and analytics across all security telemetry.

What role does the open cybersecurity schema framework (OCSF) play in building a composable siem?

OCSF provides a vendor-agnostic standard for normalizing event data across platforms. Detection logic can be written once and applied across sources. It pairs with open table formats that platforms like Databricks govern natively through Unity Catalog.

How do you integrate threat intelligence feeds and detection-as-code into a composable siem pipeline?

Ingest threat intelligence feeds as data pipelines alongside security telemetry. Version and test detection rules like software. Run enrichment and detection logic continuously against a governed data foundation.

What are the best practices for log ingestion and normalization when building a composable SIEM on a cloud data platform?

Normalize logs at ingestion time using a standard schema like OCSF. Store them in open formats such as Delta Lake, Apache Iceberg, or Parquet. Centralize governance through a unified catalog to reduce schema drift.

How can soar automation and orchestration be layered into a composable SIEM architecture?

SOAR tools connect via APIs to the composable SIEM's data and detection layers. The lakehouse serves as the shared data foundation, with response automation layered on top through orchestration platforms.

What are the cost advantages of building a composable SIEM using existing cloud data infrastructure?

Building on existing cloud infrastructure removes redundant storage and compute costs from standalone SIEM platforms. Open formats prevent vendor lock-in and data duplication, and compute scales independently from storage.

How do security teams implement real-time streaming detection and alerting in a composable SIEM?

Use a streaming pipeline layer that processes events as they arrive. Detection logic runs against fresh data the moment it lands, and alerts route to downstream tools through APIs and orchestration layers.

What open-source tools and frameworks are commonly used to assemble a composable SIEM stack?

Wazuh, Security Onion, Elastic Stack, Sigma, and the OCSF schema are commonly used. These tools can feed into a governed lakehouse foundation for centralized analytics and investigation.
Explore how Unity Catalog provides the centralized governance layer your composable SIEM needs to unify security data at scale.