Study guide

Technical reference and lesson notes

Purpose of This Lesson

This lesson explains ASIM, the Advanced Security Information Model, in Microsoft Sentinel.

ASIM matters because Microsoft Sentinel collects security data from many different sources: Microsoft products, on-premises servers, Linux systems, DNS platforms, firewalls, cloud services, endpoint tools, and custom applications. Each source may use different table names, field names, and event formats.

For a Security Operations Analyst, that creates a practical problem: how do you write detections, hunting queries, dashboards, and analytics rules that work across different log sources?

ASIM helps solve that by providing a normalization layer. Instead of forcing analysts to remember every vendor-specific field name, ASIM maps similar data into a common schema. This allows Sentinel content such as analytics rules, hunting queries, and workbooks to work more consistently across multiple sources.

For the SC-200 exam, understand ASIM as a Microsoft Sentinel feature used to normalize security data so analysts can perform cross-source detection, investigation, and hunting more efficiently.

Key Concepts

What Is ASIM?

ASIM stands for Advanced Security Information Model.

It is a normalization framework in Microsoft Sentinel that helps convert different security log formats into a consistent structure.

In plain English, ASIM helps Sentinel understand that different field names from different systems may represent the same kind of data.

For example, one DNS data source may use a field called:

DomainName
DnsName
ZoneName
Name
QueryName

Even though the names are different, they may all represent the same concept: the DNS name being queried.

ASIM helps normalize that information so analysts can query a consistent field instead of writing separate logic for every log source.

Why Normalization Matters in Microsoft Sentinel

Microsoft Sentinel is a SIEM and SOAR platform. Its value depends heavily on the quality, consistency, and usability of the data being ingested.

Without normalization, analysts may run into problems such as:

Different products using different field names for the same concept
Analytics rules only working for one specific data source
Hunting queries needing separate versions for each vendor
Workbooks showing incomplete or inconsistent results
Difficulty correlating activity across identity, endpoint, DNS, network, and cloud data

ASIM reduces this complexity by creating a common language for security data.

This is especially important in real SOC environments where logs may come from Microsoft Defender, Windows Event Logs, Sysmon, DNS servers, firewalls, Linux systems, AWS, Azure, and third-party tools.

ASIM as an Intermediary Layer

ASIM acts as a layer between:

The raw data sources
The analyst or Sentinel content using the data

A simplified flow looks like this:

Layer	Description
Data connectors	Bring data into Microsoft Sentinel from Microsoft and non-Microsoft sources
Raw tables	Store the original ingested data
ASIM parsers	Normalize source-specific fields into common schemas
Normalized schemas	Present the data using consistent field names and structures
Sentinel content	Analytics rules, hunting queries, workbooks, and investigations use the normalized data

The main idea is that Sentinel can ingest raw data from many different systems, then ASIM parsers help translate that data into a standardized format.

Cross-Source Detection

One of the biggest benefits of ASIM is cross-source detection.

Cross-source detection means writing detection logic that can work across multiple data sources instead of being tied to only one vendor or table.

For example, process creation events could come from:

Microsoft Defender for Endpoint
Windows Security Events
Sysmon
Third-party endpoint tools

Without normalization, an analyst might need separate analytics rules for each source.

With ASIM, those different sources can be normalized into a common process event schema. This makes it easier to write one detection rule or hunting query that works across multiple sources.

This is important for SC-200 because Microsoft often expects you to choose scalable, reusable security operations solutions rather than narrow one-off approaches.

Source-Agnostic Content

ASIM supports source-agnostic content.

This means Sentinel content can be written to focus on the security behavior being detected rather than the specific source that generated the log.

Examples include:

Detecting suspicious authentication activity regardless of identity provider
Hunting for DNS queries to known malicious domains
Investigating process execution across multiple endpoint log sources
Reviewing network session activity from different firewall or proxy sources

This helps SOC teams avoid duplicating the same detection logic repeatedly for every tool in the environment.

ASIM and Custom Parsers

ASIM includes built-in normalization support, but it also supports custom parsers.

A parser is a KQL-based function or query component that translates source-specific data into an ASIM-normalized format.

Custom parsers are useful when:

A data source is not fully supported out of the box
A third-party product uses unusual field names
An organization has custom applications or custom logs
A SOC team wants to standardize internally developed log sources
A customer environment has non-Microsoft platforms that still need to participate in Sentinel detections

For the exam, remember that ASIM is not limited to Microsoft-only data. It is designed to help normalize both Microsoft and non-Microsoft security events.

Normalized Schemas

ASIM includes normalized schemas for common security event categories.

Examples include:

ASIM Schema Area	What It Represents
Audit events	Administrative or configuration activity
Authentication events	Sign-ins, logons, authentication attempts
DHCP activity	IP address assignment and lease activity
DNS activity	DNS queries, responses, domains, and resolution data
File activity	File creation, deletion, modification, or access
Network sessions	Network connections and traffic sessions
Process events	Process creation and execution activity
Registry events	Windows registry modifications
User management	Account creation, deletion, modification, and group membership activity
Web sessions	Web access, proxy, URL, and HTTP-related activity

These schemas make it easier to write repeatable detections and investigations.

For example, instead of learning every possible field name for source IP address across every data source, an analyst can use normalized schema fields where available.

Query-Time Parsers

A major concept in ASIM is the query-time parser.

A query-time parser normalizes the data when the query runs.

This means the original raw data remains intact. ASIM does not rewrite or permanently modify the original log at ingestion just because a parser exists.

Benefits of query-time parsing include:

Preserves the original source data
Reduces risk of damaging or changing raw logs
Makes parser development and testing easier
Allows parser corrections without reingesting historical data
Supports flexible investigation and hunting workflows

The tradeoff is performance. If a query-time parser must process a very large dataset, the query may take longer to run.

Ingest-Time Normalization

ASIM can also support ingest-time normalization for selected events.

Ingest-time normalization means some transformation happens as data is being ingested into Sentinel.

This can improve query performance because some normalization work is already completed before the analyst runs the query.

However, ingest-time normalization should be used carefully because it changes how data is processed during ingestion.

A practical way to understand the difference:

Normalization Type	When It Happens	Main Benefit	Main Tradeoff
Query-time parsing	When the analyst runs the query	Preserves raw data and is flexible	Can be slower on large datasets
Ingest-time normalization	When data is ingested	Improves query performance	Requires more careful planning and configuration

For SC-200, remember that query-time parsers are flexible and preserve source data, while ingest-time normalization can improve performance for selected high-value data.

ASIM and Open Security Models

The lesson also connects ASIM to broader security data modeling ideas, including open-source security event metadata and common information modeling concepts.

The important point is that ASIM aligns with the general goal of standardizing security event data across systems, platforms, and operating systems.

This matters because enterprise environments are rarely Microsoft-only. A real SOC may need to correlate data from:

Windows
Linux
macOS
Cloud services
Network appliances
Endpoint tools
Firewalls
Web services
Identity providers

ASIM helps make that data more consistent and usable inside Microsoft Sentinel.

Microsoft Security Operations Context

How ASIM Fits into a SOC Workflow

ASIM is not usually the feature an analyst clicks during every investigation. Instead, it works behind the scenes to make Sentinel detections, queries, and dashboards more useful.

In a real SOC, ASIM supports:

Better alert detection
More consistent hunting queries
Cross-source correlation
Easier workbook development
Reduced query complexity
More scalable analytics rule design

Triage an Alert

During alert triage, an analyst needs to quickly understand what happened.

ASIM helps by making fields more consistent. Instead of hunting through different field names for IP addresses, accounts, hosts, domains, or processes, normalized schemas allow Sentinel content to present the data in a more predictable way.

This helps reduce triage time and improves consistency between analysts.

Investigate an Incident

During incident investigation, analysts often pivot between entities such as:

User accounts
Devices
IP addresses
DNS names
Files
Processes
URLs
Cloud resources

ASIM makes these pivots easier when the underlying logs have been normalized.

For example, if an incident involves suspicious DNS queries and process execution, ASIM can help the analyst query DNS activity and process events using consistent schemas rather than writing separate logic for each log source.

Determine Scope and Impact

A key SOC responsibility is determining how widespread an issue is.

ASIM can help answer questions such as:

Which devices queried a suspicious domain?
Which users authenticated from a suspicious IP?
Which endpoints executed a suspicious process?
Which systems communicated with a known malicious address?
Did the same behavior appear across multiple data sources?

Because ASIM supports cross-source detection, it can make scope analysis more complete.

Containment and Remediation

ASIM itself does not isolate endpoints, disable users, or remove malicious files. It helps identify and normalize the evidence that supports those actions.

Containment and remediation may still happen through tools such as:

Microsoft Defender XDR
Microsoft Defender for Endpoint
Microsoft Entra ID
Microsoft Defender for Office 365
Microsoft Defender for Cloud
Microsoft Sentinel automation rules
Logic Apps playbooks

The value of ASIM is that it helps Sentinel detect and investigate the behavior before response actions are selected.

Improve Future Detection

ASIM is useful for improving detection engineering.

A SOC team can use ASIM to:

Convert source-specific rules into source-agnostic rules
Reduce duplicate analytics rules
Improve hunting query reuse
Build workbooks that work across multiple log sources
Create detections that survive vendor or tool changes
Standardize custom data sources

This is important in mature SOC environments where detection content must be reliable, scalable, and maintainable.

Exam-Relevant Takeaways

For the SC-200 exam, remember the following:

ASIM is a Microsoft Sentinel normalization framework.
ASIM helps normalize different data sources into common schemas.
ASIM supports cross-source analytics, hunting, and workbooks.
ASIM is especially useful when different sources use different field names for similar data.
Query-time parsers normalize data when the query runs.
Query-time parsing preserves the original raw data.
Ingest-time normalization can improve performance but requires planning.
ASIM supports both Microsoft and non-Microsoft data sources.
Custom parsers can be used when a data source is not normalized out of the box.
ASIM helps analysts write more reusable and source-agnostic KQL.
ASIM does not replace data connectors; connectors still bring the data into Sentinel.
ASIM does not replace analytics rules; it makes analytics rules easier to write across multiple sources.
ASIM does not perform response actions by itself; response actions are handled by tools such as Defender XDR, Sentinel automation rules, and playbooks.

Tool / Feature Decision Guide

Scenario	Best Microsoft Security Tool or Feature	Why
You need to ingest logs from a firewall, DNS server, or cloud platform into Sentinel	Data connector	Connectors bring raw data into Microsoft Sentinel
You need to normalize different log sources that use different field names	ASIM	ASIM maps source-specific fields into common schemas
You need to detect suspicious activity across multiple endpoint data sources	ASIM-normalized analytics rule	Normalized rules can work across sources such as Defender for Endpoint, Windows Events, and Sysmon
You need to preserve original raw log data but normalize results during analysis	Query-time parser	Query-time parsers normalize when the query runs without modifying raw data
You need better query performance for selected high-volume normalized data	Ingest-time normalization	Some normalization happens during ingestion, reducing work at query time
You need to create a reusable detection that generates incidents	Analytics rule	Analytics rules run scheduled or near-real-time detections in Sentinel
You need to automatically assign, tag, or close incidents	Automation rule	Automation rules manage incident-level workflow actions
You need to trigger a complex response action such as sending Teams messages or disabling a user	Playbook	Playbooks use Azure Logic Apps for automated response workflows
You need to manually search for suspicious activity	Hunting query	Hunting queries are used for proactive investigation
You need to visualize normalized security data	Workbook	Workbooks provide dashboards and interactive visualizations
You need to compare event data against a known list, such as VIP users or suspicious domains	Watchlist	Watchlists enrich queries with external reference data
You need to investigate endpoint process activity directly	Microsoft Defender for Endpoint	Defender for Endpoint provides endpoint telemetry and response capabilities
You need to correlate security events across many products and custom data sources	Microsoft Sentinel	Sentinel is the SIEM/SOAR platform for broad data correlation

KQL Notes

The transcript does not provide a detailed KQL query, but ASIM relies heavily on KQL because parsers and Sentinel queries are KQL-based.

How KQL Relates to ASIM

In Microsoft Sentinel, ASIM parsers are commonly used through KQL functions or normalized query patterns.

Instead of querying a raw vendor-specific table directly, an analyst can query a normalized ASIM parser or schema.

The goal is to write queries based on the normalized security concept, not the original source-specific field names.

Conceptual Example: DNS Hunting with Normalized Fields

The following is a simplified example to show the idea. Function and field names may vary depending on the deployed ASIM parser and schema.

imDns
| where TimeGenerated > ago(24h)
| where DnsQuery has "suspicious-domain.com"
| summarize QueryCount = count() by SrcIpAddr, DnsQuery
| order by QueryCount desc

Query Logic

KQL Element	Purpose
`imDns`	Represents a normalized DNS parser or schema
`where TimeGenerated > ago(24h)`	Limits the query to the last 24 hours
`where DnsQuery has "suspicious-domain.com"`	Filters for DNS queries involving a suspicious domain
`summarize QueryCount = count()`	Counts how often the activity occurred
`by SrcIpAddr, DnsQuery`	Groups results by source IP and DNS query
`order by QueryCount desc`	Shows the most frequent results first

Why This Helps in a SOC

This type of query could help an analyst determine:

Which systems queried a suspicious domain
How often the domain was queried
Whether the activity is isolated or widespread
Which endpoint or network team may need to investigate further

Exam Point

For SC-200, you do not need to memorize every ASIM parser name, but you should understand the purpose of normalized schemas and why KQL queries using ASIM can be more reusable across data sources.

Common Exam Traps

Confusing Data Connectors with ASIM

A data connector brings data into Microsoft Sentinel.

ASIM normalizes data after it is available to Sentinel.

If the question asks how to ingest data, think data connector.

If the question asks how to make data from different sources use a common schema, think ASIM.

Confusing ASIM with Analytics Rules

ASIM is not a detection rule.

Analytics rules use KQL to detect suspicious behavior and generate alerts or incidents.

ASIM can make analytics rules more reusable by normalizing the data those rules query.

Confusing Query-Time Parsing with Ingest-Time Normalization

Query-time parsing happens when the query runs.

Ingest-time normalization happens when data is ingested.

If the scenario emphasizes preserving raw data and flexible parsing, query-time parsing is likely the better answer.

If the scenario emphasizes performance on selected high-volume events, ingest-time normalization may be relevant.

Assuming ASIM Is Microsoft-Only

ASIM is used in Microsoft Sentinel, but its purpose is not limited to Microsoft data.

It can help normalize data from Microsoft and non-Microsoft sources.

This matters in exam scenarios involving hybrid environments, third-party security tools, or multi-cloud data.

Choosing a Manual Query When Reusable Detection Is Needed

If the scenario asks for ongoing detection, a hunting query alone is not enough.

A hunting query is useful for manual or proactive investigation.

An analytics rule is used for recurring detection.

ASIM can support both, but the correct Sentinel feature depends on whether the requirement is investigation or continuous alerting.

Forgetting Sentinel Depends on Available Data

ASIM cannot normalize data that Sentinel does not have.

The data must first be ingested through a connector, custom log, API, agent, or another supported method.

Real-World SOC Analyst Notes

Alert Fatigue

Normalization can reduce alert fatigue by helping detection engineers write cleaner rules that work consistently across sources.

However, normalization does not automatically make detections high quality. Poorly scoped queries can still create noisy alerts.

False Positives

ASIM can help analysts query data consistently, but false positives still require tuning.

SOC teams should review:

Baseline activity
Known business processes
Service accounts
Approved tools
Expected DNS and network behavior
Normal administrative activity

Investigation Quality

Consistent field names improve investigation quality because analysts can work from a standard structure.

This is especially valuable in shift-based SOC operations where multiple analysts may investigate similar events.

Evidence Preservation

Query-time parsing helps preserve the original raw logs.

This is important because raw logs may be needed for deeper forensic review, escalation, or audit evidence.

Escalation Paths

ASIM can help identify impacted entities, but escalation still depends on the affected area.

Examples:

Finding	Likely Escalation
Suspicious process execution	Endpoint security team
Suspicious authentication	Identity team
DNS queries to malicious domains	Network or security engineering team
Suspicious cloud activity	Cloud platform team
Malicious email activity	Messaging or Defender for Office 365 team

Automation Safety

ASIM may improve detection logic, but response automation should still be carefully controlled.

Before automating containment actions, consider:

Confidence level of the detection
Potential business impact
False positive risk
Whether the action is reversible
Whether approval is required
Change control requirements
Tenant-wide or production impact

Cost Considerations

Microsoft Sentinel cost is heavily affected by data ingestion and retention.

ASIM helps make data more useful, but it does not eliminate ingestion costs.

SOC teams should consider:

Which data sources are necessary
Whether high-volume logs are worth ingesting
Retention requirements
Whether selected normalization should happen at ingest time
Whether lower-value logs should be filtered before ingestion

Quick Reference Summary

ASIM stands for Advanced Security Information Model.
ASIM is used in Microsoft Sentinel.
It normalizes data from different sources into common schemas.
It helps analysts avoid writing separate queries for every vendor-specific table.
It supports cross-source detection, hunting, analytics rules, and workbooks.
Data connectors ingest the data; ASIM normalizes the data.
Query-time parsers normalize data when the query runs.
Query-time parsing preserves original raw data.
Ingest-time normalization can improve performance for selected data.
Custom parsers can extend ASIM support to custom or third-party sources.
ASIM is useful for multi-source SOC environments.
ASIM does not perform response actions by itself.
Response actions are handled through Defender XDR, Sentinel automation rules, playbooks, and related tools.

Flashcards

Q: What does ASIM stand for?
A: Advanced Security Information Model.

Q: What Microsoft product uses ASIM for security data normalization?
A: Microsoft Sentinel.

Q: What problem does ASIM solve?
A: It normalizes different log formats and field names into common schemas so analysts can query and detect activity across multiple sources.

Q: Does ASIM ingest data into Sentinel?
A: No. Data connectors ingest data. ASIM normalizes data after it is available in Sentinel.

Q: What is cross-source detection?
A: Detection logic that works across multiple data sources instead of being tied to one specific vendor or table.

Q: What is a query-time parser?
A: A parser that normalizes data when the query runs.

Q: What is the main benefit of query-time parsing?
A: It preserves the original raw data and allows flexible parser updates.

Q: What is the tradeoff of query-time parsing?
A: It can be slower when querying very large datasets.

Q: What is ingest-time normalization?
A: Normalization that occurs as data is ingested into Sentinel.

Q: When might ingest-time normalization be useful?
A: When selected high-volume data needs better query performance.

Q: Can ASIM support non-Microsoft data sources?
A: Yes. ASIM can normalize Microsoft and non-Microsoft security data.

Q: What Sentinel features benefit from ASIM-normalized data?
A: Analytics rules, hunting queries, workbooks, and investigations.

Q: Is ASIM the same as an analytics rule?
A: No. ASIM normalizes data. Analytics rules detect suspicious behavior and generate alerts or incidents.

Q: Why is ASIM useful for SOC analysts?
A: It makes investigation, detection, and hunting more consistent across different log sources.

Q: What should you check before relying on ASIM in a Sentinel query?
A: Make sure the required data source is connected and that an appropriate parser or schema is available.

Practice Questions

Question 1:

A company ingests DNS logs from several different platforms into Microsoft Sentinel. Each platform uses different field names for the queried domain. The SOC wants to build hunting queries that work consistently across all DNS sources.

Which feature should they use?

A. Microsoft Defender for Cloud Apps
B. Microsoft Sentinel ASIM
C. Microsoft Entra Conditional Access
D. Microsoft Secure Score

Correct Answer:
B. Microsoft Sentinel ASIM

Explanation:
ASIM normalizes data from different sources into common schemas. In this scenario, it helps standardize DNS-related fields so analysts can write reusable hunting queries across multiple DNS log sources.

Question 2:

A SOC analyst wants to preserve the original raw log data but still normalize fields when running Microsoft Sentinel queries.

Which ASIM capability best matches this requirement?

A. Query-time parser
B. Ingest-time normalization
C. Microsoft Defender automated investigation
D. Watchlist enrichment

Correct Answer:
A. Query-time parser

Explanation:
Query-time parsers normalize data when the query runs, while preserving the original raw data. This is useful when flexibility and data integrity are important.

Question 3:

A detection engineer wants to create a Sentinel analytics rule that detects suspicious process execution across Defender for Endpoint, Windows Events, and Sysmon data.

What is the best design approach?

A. Create a separate rule for every source and avoid normalization
B. Use ASIM-normalized process event data where possible
C. Use Microsoft Secure Score recommendations
D. Use only Microsoft Defender for Office 365 alerts

Correct Answer:
B. Use ASIM-normalized process event data where possible

Explanation:
ASIM allows process event data from different sources to be normalized into a common schema. This supports source-agnostic analytics rules and reduces duplicate detection logic.

Question 4:

A Sentinel query using ASIM normalization is running slowly against a very large dataset. The SOC wants to improve performance for selected high-value event types while still using normalized data.

Which option should be considered?

A. Disable all data connectors
B. Use ingest-time normalization for selected events
C. Replace Sentinel with Microsoft Defender for Identity
D. Use Microsoft Entra ID Protection only

Correct Answer:
B. Use ingest-time normalization for selected events

Explanation:
Query-time parsing can be slower across large datasets because normalization happens when the query runs. Ingest-time normalization can improve performance for selected data by normalizing some elements during ingestion.

Question 5:

A security team has connected a third-party log source to Microsoft Sentinel. The data is available in a custom table, but the fields do not match Sentinel’s normalized schemas. The team wants the data to work with source-agnostic hunting queries.

What should they create?

A. A custom ASIM parser
B. A Microsoft Defender for Endpoint onboarding package
C. A Conditional Access policy
D. A Microsoft Purview retention label

Correct Answer:
A. A custom ASIM parser

Explanation:
Custom ASIM parsers can map source-specific fields from custom or third-party data sources into normalized ASIM schemas. This allows the data to participate in source-agnostic Sentinel content.

Microsoft Sentinel ASIM Normalization and Parsers – SC-200 Study Guide

Watch this lesson