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Complying with the Splunk Common Information model


Splunk's power lies in its ability to digest vast amounts of unstructured data, turn it into actionable insights, and aid organizations in making informed decisions. However, the disparateness of this data can pose challenges. This is where the importance of data normalization comes in. By aligning raw data to consistent fields and data models, normalization not only ensures that data from varied sources can be analyzed side-by-side but also simplifies the search, reporting, and alerting processes. In essence, a normalized data environment amplifies the efficiency of the Splunk platform, making it quicker and more intuitive for users to pinpoint crucial insights.

The Splunk Common Information Model (CIM) serves as a key element for unifying data from disparate sources. It is a standardized model that defines a consistent structure and naming convention for data, making it possible to draw parallels across varied datasets within the Splunk platform. Through its pre-defined data models, encompassing everything from network traffic to user authentication, it offers a template-driven approach to data interpretation.

If you aren’t already familiar with the Splunk CIM, watch this Introduction to Splunk Common Information Model video before reading any further.

Benefits of the CIM

  1. Standardized Data Models: At the heart of the Splunk CIM are predefined, out-of-the-box data models that cover a wide range of common operational domains such as network traffic, server operations, authentication processes, and others. These models provide templates for how specific kinds of data should be formatted and understood within Splunk.
  2. Consistent Field Naming: The CIM dictates consistent naming conventions for fields, which means that the same kind of data, regardless of its source, will always have the same field name in the Splunk platform. For instance, if different systems refer to an IP address with different field names, all those fields would be aligned to a common name under the CIM, ensuring consistency.
  3. Enhanced Analytical Capabilities: By adhering to the CIM, users can leverage pre-built dashboards, reports, and apps, which expect data to be in CIM format. This reduces the effort required to develop these analytical tools from scratch.
  4. Interoperability Between Apps: Many Splunk apps and solutions, especially those developed by Splunk itself, are CIM-compliant. This ensures seamless integration between different apps, allowing them to work cohesively together by interpreting data in the same manner.
  5. Facilitated Correlation: With data from different sources standardized into a consistent format, correlating events and logs across systems becomes more straightforward. For instance, correlating network logs with authentication logs to detect suspicious activity is more feasible when both datasets are CIM compliant.
  6. Ease of Adoption: For organizations new to a Splunk environment, adopting the CIM helps provide a structured approach to onboarding their data. Instead of grappling with the intricacies of diverse data sources individually, they can map their data to the established models within the CIM.

Essentially, the Splunk CIM acts as a bridge, uniting varied datasets and enabling users to harness the collective intelligence of their data ecosystem efficiently and effectively.

Challenges faced without the CIM

Inconsistent data structures and terminology can lead to a web of complexities, making efficient data analysis a difficult endeavor. The absence of a standardized model like the CIM can cause various complications, each posing its unique set of problems and implications for data professionals.

  1. Inconsistent Data Fields:
    • Problem: Different systems or data sources might represent similar information with varied terminologies. For instance, while one system might refer to a user's IP address as user_ip, another might simply call it IP. Such inconsistencies can make data indexing and searching cumbersome.
    • Impact: Analysts spend unnecessary time figuring out the correct fields or writing complex queries to ensure no data is missed. This can slow down analysis and, in worst cases, even lead to missing critical information.
  2. Time-Consuming Data Mapping Tasks:
    • Problem: Without a common framework, each new data source integrated into the Splunk platform requires custom data mapping. This involves manually determining how each field from the new source relates to existing data fields in the Splunk platform.
    • Impact: Besides being time-intensive, this approach can also introduce human errors. Over time, as more sources are added, the mapping complexity can grow exponentially.
  3. Difficulties in Correlating Data from Varied Sources:
    • Problem: Data correlation becomes a challenge when data sources use different terminologies or structures. Identifying patterns or anomalies across such diverse datasets can be a daunting.
    • Impact: Security analysts, for example, might find it hard to correlate login data with network traffic logs if the data fields are not standardized, potentially missing out on detecting security threats.

Aligning data with the CIM

Before you can align data with the CIM, you need to understand the current state of your data. This initial stage ensures that you're aware of the discrepancies and can identify the areas that require focus.

Begin with a comprehensive assessment of the data being ingested into the Splunk platform. This includes understanding the various data sources, their formats, and any inconsistencies in naming conventions or field structures. Tools like the Splunk Data Quality Dashboard (found in the Monitoring Console or Cloud Monitoring Console under indexing → Inputs → Data Quality) can assist in providing an overview of the data's current state. Analyze for missing, duplicate, or inconsistent fields that could hinder the normalization process.

Not all data sources might need alignment with the CIM, especially if they're not central to your analytical objectives. List the data sources that require normalization. Differentiate them based on their types, like logs or event data, and understand their significance in your analytics use cases. This step helps prioritize the alignment process, ensuring that critical data sources are addressed first.

In the subsequent steps, as you work towards aligning with the CIM, this pre-assessment acts as a benchmark. It enables you to gauge progress and ensure alignment objectives are consistently met.

Choosing relevant CIM models

The CIM comprises a series of predefined data models that cover a wide range of event types. Each data model represents a specific kind of activity or behavior, with each having its own set of standardized fields. Some notable examples include:

  • Network Traffic: This model is designed for data related to network activities. If you have logs from firewalls, routers, or other networking devices, this is the appropriate model. It helps in standardizing fields related to source and destination IPs, ports, protocols, and more.
  • Authentication: Ideal for logs from authentication systems, this model deals with data concerning user logins, logouts, session creation, and other related activities. Standard fields might include usernames, authentication methods, and success or failure indicators.
  • Change Management: This model is tailored for logs that detail changes within an IT environment, such as software installations, configuration alterations, or service restarts.
  • Intrusion Detection: If you have data from IDS/IPS systems, this model can help standardize fields related to detected threats, severity levels, and alerting.

The process of selecting the right CIM model involves understanding the nature and source of your logs:

  1. Data Assessment: Begin by categorizing the type of logs you have. Are they from security appliances, network devices, or application logs?
  2. Model Matching: After categorization, match your log type to the available CIM models. For instance, if you're dealing with logs from a web server, the Web data model might be the most suitable.
  3. Multiple Model Applicability: In some cases, data might fit into multiple CIM models. For instance, a device might produce both network traffic logs and security-related logs. In such situations, you might need to apply more than one data model.
  4. Custom Adjustments: While the CIM covers a broad range of log types, it might not always fit perfectly. In such cases, you might need to make minor adjustments to the model or use the CIM as a base and build upon it to meet specific needs.

Aligning data with the appropriate CIM models ensures consistency in field names, making it easier to develop dashboards, alerts, and perform cross-source data correlations. By selecting the right model(s), you're taking a significant step towards efficient and streamlined data analysis in the Splunk platform.

Data transformation

To align with standardized naming conventions in the CIM, you often need to rename or transform data fields. This ensures that your data adheres to a common naming scheme, enabling smoother integrations, more straightforward correlations across data sources, and a uniform experience when using Splunk apps that depend on the CIM.

Renaming or transforming fields to match CIM conventions

  • Field Aliasing: One of the primary techniques in making data CIM compliant is aliasing fields. For example, if your data uses src_ip and the CIM convention is src, you'd set up an alias to make src_ip recognized as src.
  • Eval-Based Transformations: Sometimes renaming isn't enough. You might need to transform field values using Splunk's eval command. For instance, if your data represents a failure with "F" and the CIM convention is "failure", you'd use an eval transformation to make this change.
  • Using Regular Expressions: For more complex transformations, especially when parsing unstructured data or when the desired fields are embedded within raw event data, Splunk's regex capabilities are useful. This can help extract and rename fields as per the standards in the CIM.

Splunk's CIM add-on assistance

  • Predefined Field Transformations: The CIM comes with a host of predefined transformations tailored for various common data sources. By leveraging these, you can save a significant amount of time as you won't have to manually define each transformation.
  • Validation and Verification: The Splunk platform has tools and ways to validate your data against CIM models, ensuring that the fields are correctly aligned and that the data is truly compliant. There are also third party tools, like SA-cim_vladiator.

While the process of data field transformation requires careful planning and execution, the Common Information Model application significantly eases the process. The end result is a Splunk environment where data from disparate sources seamlessly meshes, enabling richer insights and more effective analytics.

Validating CIM compliance

After aligning data fields to the CIM, validation acts as a checkpoint to guarantee that data adheres to the desired standards. This not only ensures consistency across datasets but also safeguards the integrity of analytics and correlations drawn from the data. Validation methods include:

  • Visual Inspection: The initial step typically involves a manual review of indexed data to see if the transformations and renaming have taken effect. This step, while basic, can quickly identify any glaring issues.
  • Data Model Inspection: You can inspect specific datasets within a data model. This allows you to see if your data is appropriately categorized and if the expected fields are present and correctly named.
  • Search-Time Verification: Write Splunk searches to specifically target the renamed or transformed fields. If the searches return the expected data, it indicates a successful transformation. For instance, if you've renamed src_ip to src as per the CIM, a search targeting the src field should return the desired results.
  • Data Model Audit Dashboard: The Data Model Audit dashboard (app/Splunk_SA_CIM/datamodel_audit) is a part of the Splunk Common Information Model Add-on. It's designed to assist administrators in assessing and ensuring CIM compliance of their data.

Maintaining CIM compliance

As organizations evolve, so does their data. New data sources might be introduced, and old ones might undergo changes. Periodic reviews ensure that all data remains aligned with CIM standards. Here are some recommendations for staying on top of CIM compliance:

  • Audit Existing Data: Regularly inspect your indexed data to identify any anomalies or deviations from CIM standards.
  • Check Source Configurations: Ensure that configurations (like props and transforms) responsible for data normalization remain effective and updated.
  • Identify New Sources: As new data sources are added, they should be assessed for CIM compliance and integrated into the Splunk ecosystem accordingly.

The CIM isn't static. It evolves to cater to changing IT landscapes, emerging threats in security, and other dynamic scenarios. Stay current by regularly reviewing and checking:

  • Splunk Documentation: Regularly check Splunk's official documentation for updates or changes to the CIM.
  • Splunk Community: Engage with the Splunk community, where users and experts often discuss updates, best practices, and nuances related to the CIM.

Updates to the CIM should be done carefully and with intention. After you're aware of changes or additions to the CIM, assess their relevance to your environment. If they apply, update your configurations and data normalization procedures accordingly. This Answers post talks about CIM upgrades and some potential issues that could arise.

Over time, ensuring that your teams understand the CIM becomes more and more important. This knowledge equips them to handle data with an eye for CIM compliance, right from the moment of data ingestion. We recommend that organizations:

  • Organize workshops where teams can get hands-on experience in implementing CIM compliance.
  • Maintain internal documentation that details your organization's approach to the CIM. This can act as a quick reference for teams.
  • Consider leveraging Splunk's official training modules or courses that focus on the CIM and data normalization.

As new team members join your organization or existing members take on new roles, ensure they're brought up to speed on CIM standards. This continuous educational loop ensures that compliance isn't compromised as personnel changes occur.

Next steps

This article is part of the Splunk Outcome Path, Reducing your infrastructure footprint. Click into that path to find more ways you can maximize your investment in Splunk software and achieve cost savings.

In addition, these resources might help you implement the guidance provided in this article: