SIEM solutions didn't work perfectly well when they were first introduced in the early 2000s, partly because of their architecture and functionality at the time but also due to the faults in the data and data sources that were fed into them.
While this approach provided an additional security layer, it failed to provide SIEM solutions with accurate data due to developers' focus on handling use cases rather than abuse cases.
They weren't experienced and didn't have the experience or knowledge to anticipate all likely attacks and write complex codes to collect or authorize access to data related to those attacks.
Many sophisticated attacks necessitated correlating events across multiple applications and data sources, which was beyond the monitoring of individual applications and their coding capabilities.
They operated within the network infrastructure and allowed admins to monitor network traffic without disrupting the flow of data to the intended destination.
The raw packet data that SPAN and TAP ports collected lacked the necessary context for effective threat detection and analysis, alongside challenges such as limited network visibility, complex configuration, and inadequate capture of encrypted traffic.
The 2000s REST API As a successor to SOAP API, REST API revolutionized data exchange with its simplicity, speed, efficiency, and statelessness.
Aligned with the rise of cloud solutions, REST API served as an ideal conduit between SIEM and cloud environments, offering standardized access to diverse data sources.
REST APIs sometimes over-fetched or under-fetched data, which resulted in inefficient data transfer between the API and the SIEM solution.
Without a strongly typed schema, SIEM solutions found it difficult to accurately map incoming data fields to the predefined schema, leading to parsing errors or data mismatches.
Because of this complexity, security analysts and admins responsible for configuring SIEM data sources found it difficult or even required additional training to handle its integrations effectively.
While some of the above data sources have not been completely scrapped out of use, their technologies have been greatly improved, and they now have seamless integrations.
It offers unparalleled scalability, empowering organizations to manage vast volumes of log data effortlessly.
It provides centralized logging and monitoring capabilities, streamlining data collection and analysis for SIEM solutions.
According to Adam Praksch, a SIEM administrator at IBM, SIEM solutions often struggle to keep pace with the rapid evolution of cloud solutions, resulting in the accumulation of irrelevant events or inaccurate data.
Notwithstanding, El Bagory acknowledged the vast potential of cloud data for SIEM solutions, emphasizing the need to explore beyond basic information from SSH logins and Chrome tabs to include data from command lines and process statistics.
This is because IoT devices are known to generate a wealth of rich data about their operations, interactions, and environments.
IoT devices, renowned for producing diverse data types such as logs, telemetry, and alerts, are considered a SIEM solutions's favorite data source.
This data diversity allows SIEM solutions to analyze different aspects of the network and identify anomalies or suspicious behavior.
While most SIEM data sources date back to the inception of the technology, they have gone through several evolution stages to make sure they are extracting accurate and meaningful data for threat detection.
This Cyber News was published on feeds.dzone.com. Publication date: Thu, 15 Feb 2024 15:43:04 +0000