Observo ai

Observo AI's core product is Singularity™ AI Data Pipelines, an AI-driven data optimization layer built into the SentinelOne Singularity platform, targeting the telemetry data management needs of security operations centers (SOCs) and DevOps.

Purpose Security telemetry data continues to grow, with official statements citing an annual growth rate of 35% and a doubling every 2 to 3 years. Traditional pipelines were not designed for such volume, leading to rising ingestion costs, slower migrations, and the forced exclusion of high-value data sources due to budget constraints, creating coverage gaps. The goal of AI Data Pipelines is to filter, normalize, and enrich data before it enters the SIEM, transforming raw telemetry into cleaner data that can be used directly by AI.

Key capabilities

1. Reduce data volume: AI automatically identifies and filters duplicate, low-value logs, and according to official statements can reduce data volume before it enters the SIEM by up to 80%, while preserving the critical signals that drive detection and investigation.
2. Simplify SIEM migration: It can be used with existing collectors without adding new infrastructure or rewriting pipelines; AI-driven normalization reduces rework and accelerates time to value for migrations. The official emphasis is on "zero pipeline rewrites," enabling migration to AI SIEM without rebuilding collectors or pipelines.
3. Extend visibility: Normalize diverse telemetry into a standardized, directly analyzable format. It provides out-of-the-box transformations and AI-driven Grok pattern generation, applies industry-standard schemas such as OCSF at the ingestion stage, and supports rapid onboarding of new data sources.
4. Improve SOC effectiveness: High-signal data helps accelerate mean time to detect (MTTD), speed up investigations, and improve detection accuracy, allowing analysts to spend less time filtering noise and more time responding.
5. Platform integration: Filtered and enriched data flows directly into AI SIEM and Singularity Data Lake, and the standardized format facilitates long-term retention, compliance, and historical analysis; cleaner data also improves the accuracy of Purple AI queries and automated investigations.

How to use it The official onboarding path includes: Connect and normalize—parse multiple telemetry formats and normalize them into standard schemas such as OCSF, so new data sources can be onboarded without manual schema development; reduce and optimize—AI-driven pattern detection identifies duplicate events and summarizes normal activity, reducing data volume while preserving high signal; migrate with confidence—evaluate and transition to AI SIEM without redeploying collectors or rebuilding pipelines, and validate detections at your own pace; monitor pipeline health—view data flows, throughput, and ingestion gaps in real time, quickly locate issues, and eliminate silent failures. The official website provides a "Get a demo" entry point, and there is a dedicated entry point for situations involving an active compromise.

Use cases It is suitable for organizations whose security operations centers handle large-scale security telemetry and want to reduce SIEM ingestion and storage costs; teams planning to migrate from traditional SIEM to AI SIEM without wanting to rebuild collectors and pipelines; analytics scenarios that need to quickly connect multiple data sources and unify them into standard formats such as OCSF; and operations scenarios that need to continuously monitor data pipeline health and avoid data gaps.

Limitations and information not yet provided The above descriptions of features and effects (such as reducing data volume by up to 80%, onboarding new data sources in hours rather than weeks, and zero pipeline rewrites) all come from official materials. Specific pricing, charging models, free trial policies, the list of supported data sources and format details, deployment environment requirements, and specific technical details beyond the OCSF, Grok pattern generation, and industry-standard schemas listed in the text are not yet provided in the materials.