This post was originally published on IT Pro Today
By Gurjeet Arora, Observo AI
Security Information and Event Management (SIEM) platforms play a crucial role in the continuous monitoring and management of security-related activities within an organization’s IT infrastructure. By collecting and analyzing security event logs and other telemetry data from a wide array of sources, SIEMs provide real-time insights into an organization’s security posture.
But SIEMs are limited in effectiveness due to a significant challenge: the ever-increasing volume of data they must process. The sheer quantity of this data is already immense and is expanding at an accelerating rate, doubling every two to three years in most organizations. The continuous data surge eventually overwhelms SIEM systems, making it difficult for security teams to effectively identify and respond to active threats and to extract meaningful security intelligence from the vast amounts of information.
Introducing the Data Pipeline
To address the challenges posed by exponential data growth while strengthening defenses, organizations are increasingly turning to data pipelines. A data pipeline is a sophisticated system designed to transform, optimize, enrich, and route telemetry data, which includes logs and metrics generated from various sources throughout an organization’s IT environment. It serves as a central nervous system for data, enabling security and DevOps teams to efficiently parse,
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