Cribl has unveiled the next evolution of its AI-native log intelligence solution, Cribl Search, which is engineered to significantly enhance security and IT operations. Built on an agentic telemetry architecture, this platform is designed for AI-driven tasks, helping organizations reduce log management expenses and quicken the response to security and IT issues.
Cribl Search integrates human-derived context with log ingestion, storage, and analysis across both Cribl-managed and external repositories, facilitating high-velocity AI queries without impacting performance or budget. As companies strive to adapt to escalating demands for productivity brought about by AI, the inflexibility and cost of traditional log systems present significant challenges. Cribl's innovative architecture provides a solution by automating data normalization and applying human context more efficiently.
Streamlined Data Normalization and Collaboration
The platform uses AI-driven parsing to automatically normalize data during ingestion, integrating machine telemetry with human inputs from tools like Jira, Git, and ServiceNow. This approach consolidates relevant data, enabling teams to collaborate and share insights within a singular workspace, thereby enhancing investigative capabilities and reducing manual tasks.
Cribl has introduced important updates to maintain performance for federated and archival queries, allowing a unified investigative surface for data across Cribl Search or existing data lakes and object stores. The redesign aims to simplify daily operations while delivering depth in investigations at AI speed.
Addressing Resource Limitations and Expanding Search Capabilities
The agentic telemetry foundation of Cribl Search is central to its performance
Francis Odum, a cybersecurity researcher at Software Analyst Cyber Research, notes the strain on resources due to the explosive growth in telemetry data. Traditional SIEM systems often fall short due to the costly nature of pre-processing data for AI applications. Cribl Search provides a federated search feature advantageous for security operations teams by cutting costs, reducing response times, and providing a clearer signal in investigative processes.
The agentic telemetry foundation of Cribl Search is central to its performance. This AI-ready infrastructure allows for efficient query processing at scale. By normalizing and enriching data at the point of entry, Cribl avoids the delays and manual work associated with schema on read approaches, supporting diverse schemas such as OCSF, OTLP, and ECS.
Customizable AI and Integrated Environments
Cribl's platform offers the flexibility to integrate teams' own AI models (BYOM), enhancing security, governance, and performance requirements. It enables the fusion of machine data with human-generated inputs, converging logs, metrics, and traces with collaborative tools to present a coherent narrative of events.
As the pioneer in fully realizing agentic telemetry, Cribl Search is designed to handle vast data sets efficiently and support investigative endeavors by both human operators and AI agents. This innovation presents a new horizon for the security sector, fostering environments where diverse data sources can be seamlessly analyzed and leveraged in real-time.
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 organizations 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. Organizations 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 centralized workspace, exponentially enhancing their investigations.
The product eliminates manual toil and complex query-building, giving organizations 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 minimize 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-optimized queries make it affordable to run the orders of magnitude more queries that AI agents generate, turning telemetry from a cost center 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.
