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Split-architecture model where Securonix hosts the analytics application while data remains in the customer's Snowflake instance.
Multi-data platform SIEM that operates as a decoupled analytics and triage layer on top of customer-managed Snowflake, Databricks, and S3 data lakes.
Cloud-native SIEM that runs as a security analytics layer on top of customer-owned Snowflake, Databricks, or AWS S3 storage.
Connected-app model where security data is ingested into and resides within the customer's own Snowflake account.
Native SIEM built on Databricks that decouples storage and compute, storing logs in open formats (Iceberg/Delta Lake) within the customer's cloud account.
S3-native security data lake and search engine that builds lightweight indexes alongside logs in the customer's own S3 buckets.
Open-source S3-native SIEM built on Apache Iceberg, using a decoupled architecture where logs and indexes reside in the customer's AWS S3 bucket.
BYOC (Bring Your Own Cloud) model that decouples compute from data, storing logs in the customer's own cloud object storage.
Serverless, stateless architecture that separates indexing and search tiers, storing all primary data in customer-accessible cloud object stores.
Search-in-place architecture that queries security data directly within customer-controlled storage (S3, ADLS, Snowflake, etc.) without rehydration.
Cloud SIEM with a decoupled 'Data Lake' tier (Auxiliary logs) that stores large-volume logs in customer-managed Azure Data Lake Storage (ADLS).
SIEM platform utilizing an index-free architecture on LogScale with federated search capabilities to query data residing in customer-controlled AWS S3 buckets.
SaaS-based SIEM platform featuring a federated search architecture (Data Explorer) that queries disparate logs directly in customer data lakes and cloud storage.
Federated search overlay that decouples the query/analytics layer from the underlying storage, searching data in-place across customer-controlled data lakes and S3 buckets.
Connected-app architecture where security analytics and detection logic run as a layer on top of the customer's owned Snowflake security data lake.
Hyper-scalable security data lake architecture that separates compute from long-term storage, supporting data residency in customer-managed cloud object stores.
Decoupled search architecture where the Splunk Cloud compute tier performs remote queries directly against security data stored in the customer's own AWS S3 buckets.
Open XDR platform that allows security event data to be normalized and synchronized to a customer-controlled external data lake.
SIEM with a configurable Data Lake backend that routes logs to customer-managed object storage (S3, GCS, or Azure Blob).
AI-driven SIEM platform that decouples detection from storage, searching data in its native format across cloud and hybrid environments.
API-first SecOps Cloud Platform that decouples telemetry ingestion and storage, supporting data routing and search across customer-controlled cloud infrastructure.
Cloud security analytics solution that exports findings and telemetry into the customer's Snowflake security data lake for long-term retention and correlation.
Cloud-native SIEM built on a security data lake architecture that supports integration with customer-managed Amazon Security Lake and S3 storage.
Cloud SIEM and XDR platform that integrates with customer-controlled Amazon Security Lake (S3) to query and analyze centralized security telemetry.
SecOps platform with a decoupled network log archive (Lumu Archive) that stores and analyzes historical logs for long-term retention and retrospective hunting.
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