Cribl has unveiled the latest iteration of its AI-native log intelligence solution, Cribl Search, offering enhanced capabilities for telemetry search.
This new platform, designed with AI-driven workloads in mind, aims to help security and IT operations teams reduce log management costs, expedite incident resolution, and streamline their tools. Integrating log ingest, storage, and analysis, Cribl Search allows for high-speed queries across both Cribl-managed and external data repositories without sacrificing performance or cost efficiency.
Automating data normalisation
The platform's agentic telemetry architecture enables automatic data normalisation at ingestion, merging machine telemetry with human input from tools like Jira, Git, and ServiceNow. This unity offers teams a centralised workspace to collaborate effectively, enhancing investigations through federated analysis across diverse data sources.
Clint Sharp, Cribl's co-founder and CEO, stated, "The age of agentic AI is here, driving expectations for productivity sky-high, but legacy log platforms are simply not equipped to handle the exponential data and query growth from AI agents. Organisations are constrained by platforms that are too expensive, too rigid, and too fragmented to keep up."
Single investigation surface
It supports federated queries, allowing analysis across local data lakes and external object stores
Cribl Search creates a unified investigative environment that removes the complexities of manual query-building, providing deeper insights at AI speeds.
It supports federated queries, allowing analysis across local data lakes and external object stores, enhancing performance with continuous updates since its initial release. The product's simplification of operations is especially beneficial for security operations (SecOps) teams facing data volume challenges and resource limitations.
Limited analyst resources
Cybersecurity researcher Francis Odum highlighted the constraints legacy SIEMS face in handling AI-driven data growth due to prohibitive data normalisation costs.
"Cribl Search offers significant advantages for SecOps teams facing the challenges of rapidly growing data volumes, and limited analyst resources. The platform&rsqauo;s federated search capability enables teams to reduce costs, lower latency, and minimise noise, while still maintaining the ability to retrieve and enrich data with relevant context precisely when needed for investigations or compliance," Odum remarked.
AI-ready infrastructure
The innovative design of Cribl Search, with its agentic telemetry and AI-ready infrastructure, enables structured data handling and agent-optimised queries. This set-up transforms telemetry into a resource for continuous analysis, addressing challenges such as schema on read delays and parser management.
Cribl's solution allows flexibility with schema-agnostic and federated analysis capabilities while providing the option for teams to use their own AI models (BYOM) for tailored performance and security. By integrating machine data and human context, the platform provides a comprehensive understanding of various operational narratives.
Cribl, the AI Platform for Telemetry, announces its next generation of Cribl Search, an AI-native log intelligence solution built on an agentic telemetry architecture that gives security and IT operations teams significantly greater impact.
Designed from the ground up for AI driven workloads, the new Search experience helps organisations cut log management costs, resolve security and IT incidents faster, and streamline tooling. Cribl Search unifies human-generated context with log ingest, storage, and analysis across both Cribl managed and external data stores for AI speed query volumes without compromising performance or cost.
Automating data normalisation
"The age of agentic AI is here, driving expectations for productivity sky-high, but legacy log platforms are simply not equipped to handle the exponential data and query growth from AI agents. Organisations are constrained by platforms that are too expensive, too rigid, and too fragmented to keep up," said Clint Sharp, co-founder & CEO of Cribl.
"Cribl Search, built on agentic telemetry, is the essential new architecture that closes this gap, doubling speed while empowering security and IT teams to automate data normalisation, add human context to any situation, and move at the speed of AI."
Single investigation surface
Using AI-powered parsing, Cribl Search automatically normalises data at ingest and unifies machine telemetry with human context from tools such as Jira, Git, and ServiceNow so all relevant signals land in one place. Teams can collaborate and share knowledge in a centralised workspace, exponentially enhancing their investigations.
The product eliminates manual toil and complex query-building, giving organisations investigative depth at AI-speed with a far simpler day to day operating model. Federated analysis provides agents and humans a single investigation surface over all data, whether it lives in Cribl Search or in existing lakes and object stores. Performance is a key consideration for federated and archival queries, and Cribl has introduced significant enhancements to continuously improve performance since Search was initially introduced.
Limited analyst resources
"The growth of telemetry data has outpaced company resources and legacy SIEMS have not kept up with AI because the cost of normalising data ahead of time is prohibitive,” said Francis Odum, cybersecurity researcher at Software Analyst Cyber Research.
“Cribl Search offers significant advantages for SecOps teams facing the challenges of rapidly growing data volumes, and limited analyst resources. The platform’s federated search capability enables teams to reduce costs, lower latency, and minimise noise, while still maintaining the ability to retrieve and enrich data with relevant context precisely when needed for investigations or compliance.”
AI-ready infrastructure
The key to achieving these results is the foundational architecture of the new Cribl Search experience: agentic telemetry. This revolutionary design combines AI-ready infrastructure, agentic AI, machine telemetry, and human-generated data to deliver unique capabilities. Agentic telemetry uniquely:
- Delivers AI speed performance: Structured data lakes and agent-optimised queries make it affordable to run the orders of magnitude more queries that AI agents generate, turning telemetry from a cost centre into continuous analytic fuel.
- Structures data at ingestion: Agentic AI normalises and enriches data on the way in, avoiding brittle, slow schema on read delays and eliminating the toil of manually created parsers.
- Remains schema agnostic and federated: Analysis works across OCSF, OTLP, ECS, and custom schemas, and can federate queries across local datasets and external stores so teams aren’t locked into a single format, location, or vendor solution.
- Bring your own Model (BYOM): Teams can power the AI capabilities of Search with their own approved AI model instead of a Cribl-managed backend, giving them the choice and control to use a model that meets their performance, security, and governance requirements.
- Uses agentic AI to fuse machine data and human context: Logs, metrics, and traces are combined with tickets, runbooks, pull requests, Slack, and other collaboration and change systems to build an explainable story of cause and effect.
Cribl Search is the first offering to fully embody agentic telemetry end-to-end. It achieves this by getting data in, optimising storage for high-speed analysis, and powering investigations for both humans and agents within an AI-driven workspace across teams.
