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Azure Sentinel is a scalable, cloud-native, security information event management (SIEM) and security orchestration automated response (SOAR) solution. Azure Sentinel delivers intelligent security analytics and threat intelligence across the enterprise, providing a single solution for alert detection, threat visibility, proactive hunting, and threat response. Critical to its operation is the ability to ingest data from various sources, which is where Azure Sentinel Information Model (ASIM) parsers come into play.
ASIM parsers standardize and normalize data from various sources, making it easier for security analysts to query, visualize, and analyze the data. The ASIM normalization process converts disparate log formats into a common schema, enabling cross-platform security analytics. It allows analysts to write queries once and apply them across various data sources without the need to account for the peculiarities of each source.
To develop an ASIM parser, you’ll follow these steps:
Managing ASIM parsers involves regularly reviewing your parsers to ensure they continue to operate as expected. These tasks often include:
Imagine you are developing an ASIM parser for a fictitious web server’s logs. Here’s an outline of the process:
create function with (folder = “ASIM”, docstring=”Web Server ASIM Parser”, skipvalidation=”true”)
WebServer_ASIM_Parser()
{
WebServerLogs // Your table where logs are initially ingested
| extend EventTime = todatetime(timestamp),
SourceIP = tostring(src_ip),
HttpRequestMethod = tostring(request_type),
Url = tostring(url),
UserAgent = tostring(user_agent)
// … more field mappings
}
Managing parsers entails:
ASIM parsers are pivotal in creating a unified security posture within the Azure Sentinel platform. Through careful development and ongoing management, these parsers enable security analysts to work more efficiently and with greater confidence in their data, ultimately contributing to enhanced security operations and incident response efforts.
True
ASIM parsers are indeed used to normalize and transform data from disparate sources into a common schema within Microsoft Sentinel, which allows for easier analysis and querying.
D. All of the above
Time, IP address, and username normalization are all crucial aspects of ASIM that make it possible to correlate events effectively and detect potential threats.
False
ASIM parsers are not limited to Microsoft products and services; they are designed to work with various log types from different sources including non-Microsoft products and services.
B. KQL (Kusto Query Language)
Kusto Query Language (KQL) is the primary language used for crafting ASIM parsers and queries within Microsoft Sentinel.
False
ASIM parsers require regular maintenance and updates to ensure they keep up with changes in log formats and remain effective in parsing new data sources.
B. To normalize disparate log data formats
The purpose of using ASIM parsers is to normalize different log data formats from various sources into a standardized schema for easy querying and threat detection.
True
One of the key benefits of ASIM is that it allows for generic detection queries to be written that work across different types of data sources, thanks to the normalization process.
D. All of the above
When developing an ASIM parser, it is important to consider the data source schema, the standardized ASIM schema, and the specific use cases and requirements to ensure effective parsing and compatibility.
False
ASIM parsers are optional and are not a strict requirement for consuming data from Microsoft 365 Defender in Microsoft Sentinel, but they help in normalizing data for consistent analysis.
D. A and B
Templates and examples of ASIM parsers can typically be found on GitHub repositories and within Microsoft’s official documentation, providing references for developing and managing parsers.
False
ASIM parsers can be applied to real-time streaming data as well as to the data that has already been ingested into Microsoft Sentinel, allowing for flexible parsing options.
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