Continuing our normalization journey, we added to the networking and DNS schemas the Authentication, Process
Events, and Registry Events schemas and delivered normalized
content based on the two. We also added ARM template deployment and support
for Microsoft Defender for Endpoints to the Network
Special thanks to @Yuval Naor , @Yaron
Fruchtmann , and @Batami Gold , who made all this possible.
Why should you
source detection: Normalized Authentication analytic rules work across
sources, on-prem and cloud, now detecting attacks such as brute force or
impossible travel across systems including Okta, AWS, and Azure.
process event analytics support any source that a customer may use to
bring in the data, including Defender for Endpoint, Windows Events, and
Sysmon. We are ready to add Sysmon for Linux and WEF once released!
- EDR support: Process,
Registry, Network, and Authentication consist the core of EDR event
The Network Schema introduced last year is now
easier to use with a single-click ARM template deployment.
Deploy the Authentication, Process Events,
Events, or Network Session parser packs in a single click using
Jon us to learn more about the Azure Sentinel information model in two webinars:
Information Model: Understanding Normalization in Azure Sentinel
- Deep Dive into Azure Sentinel Normalizing
Parsers and Normalized Content
Why normalization, and what is the Azure Sentinel
Working with various data types and tables together presents a challenge.
You must become familiar with many different data types and schemas, write
and use a unique set of analytics rules, workbooks, and hunting queries for
each, even for those that share commonalities (for example, DNS servers).
Correlation between the different data types necessary for investigation and
hunting is also tricky.
The Azure Sentinel Information Model (ASIM) provides a seamless experience
for handling various sources in uniform, normalized views. ASIM aligns with
the Open-Source Security Events Metadata (OSSEM) common
information model, promoting vendor agnostic, industry-wide normalization.
- Allows source agnostic
content and solutions
- Simplifies analyst use
of the data in sentinel workspaces
The current implementation is based on query time normalization using KQL
functions. And includes the following:
standard sets of predictable event types that are easy to work with and
build unified capabilities. The schema defines which fields should
represent an event, a normalized column naming convention, and a standard
format for the field values.
- Parsers map existing data
to the normalized schemas. Parsers are implemented using KQL functions.
for each normalized schema includes analytics rules, workbooks, hunting
queries, and additional content. This content works on any normalized data
without the need to create source-specific content.