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In the modern enterprise, data can exist in any number of different places, including cloud-based applications, edge locations, and on-premise services. The dispersion of data across the enterprise is a major challenge for IT and security teams looking to gather, monitor, and process data.
To help enterprises have better observability of their data, Cribl, a data engine for IT and security, today unveiled Cribl Copilot, an AI-powered productivity accelerator for data management.
The new tool, which was unveiled at CriblCon, the company’s annual worldwide user event, is designed to streamline the efficiency of telemetry data management, bridge the skills gap in IT and security, and enable efficient scaling of resources.
The Cribl Copilot acts as an “AI-powered engineering partner” to tackle the most complex IT and security data challenges. Available across the entire Cribl product lineup, the new tool integrates seamlessly with Cribl Edge, Cribl Stream, Cribl Search, and Cribl Lake.
One of Cribl Copilot’s key features is its ability to understand the intricacies of complex data structures, enabling users to create immediate value and start extracting valuable data in a matter of minutes.
Founded in 2018, Cribl was launched as a data observability pipeline provider but later expanded its offering to include Cribl Search and other data discovery tools.
Earlier this year, Cribl added a new data lake service to its product lineup to provide more control and flexibility to IT and security teams. With the evolution into a multi-product company, Cribl aims to offer more than just observability solutions.
Rather than competing with large data platform vendors such as Databricks and Snowflake, Cribl is positioning itself as a data engine for IT and security, specializing in managing large volumes of data for security and observability use cases.
“There is a fundamental tension between the rate of data growth and the limited resources used to manage that data, leaving IT and security teams without the tools, time, or expertise to effectively configure and maintain new systems,” said Clint Sharp, co-founder and CEO of Cribl.
According to Sharp, Cribl Copilot has the capability to understand the enterprise environment and deployment models of large-scale data infrastructure, offering intelligent suggestions, generating complex configurations in seconds, and automating routine tasks. This provides security and IT teams the control they need to optimize data strategies and boost productivity.
Cribl Copilot is based on using a Retrieval Augmented Generation (RAG) approach that involves the use of vector databases to access the company’s knowledge base. The capabilities also include auto-generation of insights, notifications, and dashboards based on enterprise data and system metadata. There is also a dedicated “support guru” to help build AI-powered troubleshooting tools.
Cribl claims that Copilot can generate code functions, pipelines, and Kusto Query Language (KQL) using natural language. This helps reduce learning curves to minutes while enabling users to create complex observability pipeline configurations and service queries. Additionally, users can chat with Cribl Copilot to tap into its solution-engineering expertise to find solutions to the most complex data challenges.
In his keynote address at the CriblCon, Sharp announced enhancements to other Cribl products including extended scalabilities for Cribl Edge, support for time-series databases, and cloud warehouses for Cribl Search.
The introduction of Cribl Copilot comes at a crucial time as IT and security teams face increasing challenges with data growth and resource limitations. By enabling IT and security teams to easily tackle complex data challenges without learning curve delays and bridging the skills gap, Cribl Copilot empowers teams to enhance efficiency and maintain robust security postures.
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