Study guide
10.1 The Splunk Common Information Model (CIM) & Normalization
Splunk Core Certified Power User Exam
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1 Introduction & Exam Overview
0.1 Exam Facts, Logistics & Test-Taking Strategy 0.2 Splunk Knowledge Objects & Search Pipeline Architecture
2 Chapter 1: Transforming Commands for Visualizations
1.1 The chart Command & Multi-Dimensional Aggregations 1.2 The timechart Command & Time-Series Visualizations 1.3 Transforming Options: limit, useother, usenull, and Split-By Clauses
3 Chapter 2: Filtering and Formatting Results
2.1 The eval Command & Core Evaluation Functions 2.2 Filtering Data: search vs. where Commands 2.3 Handling Missing Data with fillnull
4 Chapter 3: Correlating Events & Transactions
3.1 Understanding Transactions & Event Grouping Concepts 3.2 Configuring transaction Constraints: maxspan, maxpause, startswith, endswith 3.3 Analyzing Transaction Output Fields: duration & eventcount 3.4 transaction vs. stats: Performance & Architectural Trade-offs
5 Chapter 4: Creating and Managing Fields
4.1 Regular Expression Extractions with Field Extractor 4.2 Delimiter-Based Extractions with Field Extractor 4.3 Field Extraction Management, Permissions & Search-Time Precedence
6 Chapter 5: Field Aliases and Calculated Fields
5.1 Creating and Using Field Aliases 5.2 Creating and Configuring Calculated Fields 5.3 Aliases vs. Calculated Fields in the Search Execution Pipeline
7 Chapter 6: Tags and Event Types
6.1 Creating and Using Event Types 6.2 Creating and Managing Tags 6.3 Combining Tags and Event Types for Data Classification
8 Chapter 7: Creating and Using Search Macros
7.1 Creating Basic Search Macros & Backtick Syntax 7.2 Macro Arguments, Validation & Evaluation Functions 7.3 Macro Scoping, Permissions & Troubleshooting
9 Chapter 8: Creating and Using Workflow Actions
8.1 Workflow Action Architecture & Field/Event Context 8.2 Creating GET and POST Workflow Actions 8.3 Creating Search Workflow Actions & Dynamic Secondary Searches
10 Chapter 9: Data Models and Pivot
9.1 Data Model Architecture & The Pivot Tool 9.2 Creating and Structuring Datasets & Hierarchies 9.3 Accelerating Data Models & TSIDX Acceleration
11 Chapter 10: Common Information Model (CIM)
10.1 The Splunk Common Information Model (CIM) & Normalization 10.2 CIM Knowledge Objects, Data Models & Schemas 10.3 Normalizing & Validating Data with the CIM App / datamodel command
10.1 The Splunk Common Information Model (CIM) & Normalization
Key Takeaways Explain This Topic AI
The Splunk Common Information Model (CIM) is a standardized taxonomy, shared methodology, and collection of domain-specific data model schemas that normalize heterogeneous machine data across multi-vendor environments into uniform field names, event classifications, and values.
The Splunk CIM Add-on (Splunk_SA_CIM) packages standard data model schemas, event tags, field aliases, lookups, and search macros, providing the target data architecture required across enterprise Splunk deployments.
CIM normalization eliminates the N × M multi-vendor maintenance complexity by converting proprietary vendor terminologies into a uniform 1 × M standard, enabling cross-technology analytics, universal dashboards, and correlation searches.
Splunk premium solutions, including Splunk Enterprise Security (ES) and Splunk IT Service Intelligence (ITSI), depend entirely on CIM-compliant datasets to power correlation searches, Analytic Stories, Risk-Based Alerting (RBA), and Key Performance Indicators (KPIs).
CIM enforces strict taxonomy rules: all field names must be lowercase snake_case, event action values must be standardized (e.g., allowed/blocked, success/failure), and source (src), destination (dest), and reporting device (dvc) must be strictly distinguished.
Last updated: August 2026
10.1 The Splunk Common Information Model (CIM) & Normalization
Quick Summary: In modern enterprise IT and Security Operations Center (SOC) environments, machine data streams in continuously from dozens or hundreds of disparate hardware appliances, operating systems, cloud providers, and software applications. While these heterogeneous technologies record fundamentally identical computing activities—such as network connections, user authentications, web transactions, and administrative changes—each vendor formats its logs using proprietary terminology, non-standard field names, and distinct status codes. The Splunk Common Information Model (CIM) resolves this architectural challenge by providing a universal data taxonomy, standardized field names, and pre-packaged data model schemas that normalize diverse machine data into uniform, interoperable datasets at search time without altering raw data.
1. The Multi-Vendor Heterogeneity Problem ( N × M N \times M N × M Complexity)
Consider an enterprise security operations team tasked with monitoring authentication activity across an organization. User login and credential verification events originate from diverse systems: Active Directory domain controllers, Okta Identity Cloud, Cisco AnyConnect VPN concentrators, Linux SSH daemons, and AWS CloudTrail. +---------------------------------------------------------------------------------------------------------+
- | THE MULTI-VENDOR TERMINOLOGY PROBLEM |
- | |
- | Active Directory: TargetUserName="jsmith" WorkstationName="10.1.5.20" EventCode=4625 (Failure) |
- | Okta SSO: actor.alternateId="jsmith" client.ipAddress="10.1.5.20" outcome.result="FAILURE" |
- | Linux auth.log: user="jsmith" rhost="10.1.5.20" "Failed password" |
- | Cisco ASA VPN: user="jsmith" src_ip="10.1.5.20" msg_id=113039 (Rejected) |
- | AWS CloudTrail: userIdentity.userName="jsmith" sourceIPAddress="10.1.5.20" responseElements=null |
- +---------------------------------------------------------------------------------------------------------+
- The Operational Cost of Unnormalized Data
Without a standardized schema layer, an analyst attempting to detect brute-force password spraying across the entire enterprise must author a complex query with nested boolean logic and explicit vendor field handling: `-- Unnormalized multi-vendor authentication search --`
- (index=win_auth EventCode=4625 TargetUserName="*")
- OR (index=okta outcome.result="FAILURE" actor.alternateId="*")
- OR (index=os_linux "Failed password" sourcetype="syslog")
- OR (index=cisco_vpn sourcetype="cisco:asa" msg_id=113039)
- OR (index=aws_cloudtrail eventName="ConsoleLogin" errorMessage="*")
- | eval target_user = coalesce(TargetUserName, 'actor.alternateId', user, 'userIdentity.userName')
- | eval client_ip = coalesce(WorkstationName, 'client.ipAddress', rhost, src_ip, sourceIPAddress)
- | stats count AS failed_attempts by target_user, client_ip
- | where failed_attempts > 10
This unnormalized architecture creates severe operational liabilities in enterprise environments:
N × M N \times M N × M Maintenance Burden: When an enterprise uses N N N vendor data sources and implements M M M detection use cases (or dashboards), engineers must maintain N × M N \times M N × M query variations. Adding a single new authentication provider requires updating every dashboard, report, and correlation search across the organization.
Sluggish Search Performance: Massive boolean OR searches spanning dissimilar field names cannot leverage accelerated TSIDX summaries efficiently, consuming excessive CPU and disk I/O.
Fragile Detection Logic: Vendor firmware upgrades that alter a single field name (such as renaming client.ipAddress to client.ip_address ) silently break security detections without warning.
Application Incompatibility: Turn-key analytics applications, including Splunk Enterprise Security (ES) and Splunk IT Service Intelligence (ITSI), cannot function without standardized data structures.
The CIM Solution ( 1 × M 1 \times M 1 × M Efficiency)
CIM transforms this scaling bottleneck into a clean 1 × M 1 \times M 1 × M architecture. Each incoming data source is normalized once at search time using field extractions, field aliases, calculated fields, event types, and tags (packaged in a Technology Add-on (TA) ). Once normalized, every security search, report, and dashboard queries standardized CIM fields ( user , src , dest , action ) across all vendors simultaneously: `-- Normalized CIM query across all vendors --`
- tag=authentication action=failure
- | stats count AS failed_attempts by user, src
- | where failed_attempts > 10
2. What is the Splunk Common Information Model (CIM)?
The Splunk Common Information Model (CIM) is a shared taxonomy, methodology, and set of domain-specific data model schemas designed to structure and standardize machine data. Rather than enforcing rigid schema-on-write constraints that modify raw log data on disk, CIM leverages Splunk's schema-on-read architecture. Raw logs remain immutable, while search-time knowledge objects project a standardized schema over the data when queried. +-----------------------------------------------------------------------------------------+
- | CONTENTS OF Splunk_SA_CIM |
- | |
- | 1. Pre-Packaged Data Models: JSON/XML schema definitions (Authentication, |
- | Network_Traffic, Web, Endpoint, Change, Email, Malware, Alerts, etc.) |
- | 2. Standard Event Tags & Types: Base tag schemas in tags.conf & eventtypes.conf |
- | 3. Search Macros: Standardized search filters (e.g., `cim_corporate_web_domain_search`)|
- | 4. Reference Lookups: Protocol mappings, port classifications, severity lookups |
- | 5. Acceleration Architecture: Pre-configured TSIDX acceleration settings |
- +-----------------------------------------------------------------------------------------+
- Architectural Deployment of Splunk_SA_CIM
The CIM is distributed as the Splunk Common Information Model Add-on (folder name: Splunk_SA_CIM ), freely available on Splunkbase. In a distributed Splunk deployment, Splunk_SA_CIM is deployed across specific architectural tiers:
Search Heads: Mandatory. Search heads use Splunk_SA_CIM to evaluate search-time knowledge objects, generate Pivot interfaces, expand search macros, and execute CIM-based SPL.
Indexers (Search Peers): Mandatory when Data Model Acceleration (DMA) is enabled. Indexers construct and maintain columnar Time-Series Index ( .tsidx ) summary files on local disk for accelerated CIM models.
Heavy Forwarders & Universal Forwarders: Typically not required, unless index-time field extractions or heavy transformation routing occurs on the forwarder tier.
Important
TAs vs. Splunk_SA_CIM : A critical distinction tested on the Power User exam is that Splunk_SA_CIM does not contain vendor-specific field extractions. Splunk_SA_CIM defines the target standard (the schemas, data models, and tags). Individual vendor Technology Add-ons (TAs) (such as Splunk_TA_paloalto , Splunk_TA_windows , or Splunk_TA_cisco-asa ) contain the source mappings (the vendor-specific regex extractions, field aliases, and event types) that map proprietary logs into the CIM schemas.
3. Why CIM Normalization Matters: Enterprise Ecosystem Value
CIM normalization is not merely an aesthetic formatting convention; it is the foundational data contract powering Splunk's entire enterprise application ecosystem.
1. Splunk Enterprise Security (ES)
Splunk Enterprise Security is built entirely on CIM. Every out-of-the-box Correlation Search , Analytic Story , Asset & Identity correlation framework, Incident Review dashboard, and Risk-Based Alerting (RBA) risk rule queries CIM data models using high-performance | tstats searches.
If incoming firewall logs are not normalized to the Network_Traffic data model ( src , dest , bytes , action ), ES network threat detection rules return zero findings.
If endpoint telemetry is not mapped to the Endpoint or Malware schemas, ES threat intelligence matching cannot correlate Indicators of Compromise (IOCs) against enterprise host activity.
2. Splunk IT Service Intelligence (ITSI)
Splunk ITSI relies on CIM to facilitate entity discovery, service topology modeling, and Key Performance Indicators (KPIs) . When compute instances, virtualization clusters, storage arrays, and network devices normalize telemetry to CIM performance schemas ( CPU , Memory , Storage , Network ), ITSI automatically aggregates multi-vendor infrastructure health into composite service health scores.
3. Out-of-the-Box Content & Portability
When knowledge objects conform to CIM:
Splunkbase security apps, compliance packs (PCI-DSS, HIPAA, SOX), and custom dashboards can be shared across business units without altering SPL.
Migrating between hardware vendors (e.g., replacing Cisco firewalls with Palo Alto Networks, or Symantec with CrowdStrike) requires zero updates to correlation rules or executive dashboards—only the ingestion TA changes.
4. CIM Normalization Mapping Table Across Major Vendor Logs
The table below demonstrates how disparate vendor-specific field names from major technology platforms are normalized into unified CIM standard fields:
CIM Standard Field Data Type Schema Description Palo Alto Networks Cisco ASA Windows Security Event Linux syslog / auth AWS CloudTrail / VPC
src String Source endpoint identifier (FQDN, host, IP, or MAC). pan_src_ip src_ip WorkstationName rhost sourceIPAddress
src_ip String (IP) Specific IPv4 or IPv6 source address of the initiator. pan_src_ip src_ip IpAddress rhost srcaddr (VPC Flow)
src_port Integer Source TCP/UDP port number (1–65535). pan_sport src_port IpPort rport srcport (VPC Flow)
dest String Target destination endpoint identifier (FQDN, IP, host). pan_dest_ip dest_ip Computer host recipientAccountId
dest_ip String (IP) Specific IPv4 or IPv6 destination address. pan_dest_ip dest_ip DestIpAddress dest_ip dstaddr (VPC Flow)
dest_port Integer Destination TCP/UDP service port number. pan_dport dest_port DestPort dest_port dstport (VPC Flow)
dvc String The reporting intermediary device, sensor, or middlebox. pan_device_name dvc / host Computer host eventSource
user String The authenticated, target, or affected user identity. pan_src_user user TargetUserName user userIdentity.userName
src_user String The initiating user when distinct from the target user. pan_src_user src_user SubjectUserName src_user / sudo_user userIdentity.principalId
app String Layer 7 application protocol, process, or service. pan_app service ProcessName process eventName
action String Standardized event outcome ( allowed , blocked , success , failure ). action ( allow -> allowed ) action ( built -> allowed ) Derived ( 4624 -> success , 4625 -> failure ) Derived ( Accepted -> success ) Derived ( errorCode present -> failure )
bytes Integer Total network payload volume in bytes ( bytes_in + bytes_out ). pan_bytes bytes TotalBytes bytes bytes (VPC Flow)
bytes_in Integer Volume of network traffic received/inbound in bytes. pan_bytes_in rcvd_bytes BytesReceived bytes_in Derived (VPC Flow)
bytes_out Integer Volume of network traffic sent/outbound in bytes. pan_bytes_out sent_bytes BytesSent bytes_out Derived (VPC Flow)
duration Number Elapsed time for the transaction or session (seconds). pan_elapsed_time duration SessionDuration duration Derived (VPC Flow)
signature String Rule name, alert title, error code, or vulnerability ID. pan_threat_id msg_id EventCode message errorCode
vendor_product String The vendor name and product name generating the log. Derived ( "Palo Alto PAN-OS" ) Derived ( "Cisco ASA" ) Derived ( "Microsoft Windows" ) Derived ( "Linux Syslog" ) Derived ( "AWS CloudTrail" )
5. CIM Field Naming Conventions & Core Taxonomy Rules
The CIM enforces strict, non-negotiable standards for field names, field casing, and categorical values.
1. Strict Lowercase & Snake_Case
All CIM field names must be strictly lowercase. Multi-word fields must use underscores ( _ ) as word separators (e.g., src_ip , dest_port , http_user_agent , bytes_in ). Splunk search-time field names are case-sensitive; using Src_IP , ClientIP , or src-ip breaks CIM compliance and will cause data model queries to drop the field.
2. Standardized Action Values
Vendor status strings and outcome verbs must be transformed into standardized, lowercase categorical values:
Network / Proxy / Access Control: action must be one of allowed , blocked , deferred , or unknown .
Authentication: action must be success or failure .
Malware / Endpoint Containment: action must be blocked , allowed , quarantined , deleted , cleaned , or unknown .
3. Standardized Units
Timestamps: Represented in epoch seconds ( _time ).
Network Volumes: Represented in integer bytes ( bytes , bytes_in , bytes_out ).
Durations: Represented in seconds or milliseconds as defined by the specific domain schema.
4. Critical Conceptual Distinction: src vs. dest vs. dvc
A frequent source of confusion on certification exams and in production data engineering is distinguishing between src , dest , and dvc :
src (Source): The endpoint initiating the network connection, request, or transaction (e.g., the employee laptop 10.1.5.20 ).
dest (Destination): The endpoint receiving the connection or targeted by the transaction (e.g., the web server 192.168.10.50 ).
dvc (Device): The intermediary network device, sensor, or middlebox that observed, routed, or inspected the traffic (e.g., the edge firewall fw-edge-01.corp at 10.1.1.1 ). +-------------------------------------------------------------------------------------------------+
- | DISTINGUISHING SRC, DEST, AND DVC |
- | |
- | [ Client Laptop ] ─────────────> [ Edge Firewall ] ─────────────> [ Web Server ] |
- | 10.1.5.20 10.1.1.1 192.168.10.50 |
- | |
- | CIM: src = 10.1.5.20 CIM: dvc = fw-edge-01 CIM: dest = 192.168.10.50|
- | src_ip = 10.1.5.20 dvc_ip = 10.1.1.1 dest_ip = 192.168.10.50|
- | src_port = 54321 dest_port = 443 |
- +-------------------------------------------------------------------------------------------------+