Creating Indexes While Balancing Least Privilege and Search Efficiency for Security Service Providers

[nateloepker]

Hello,

I'm am wondering how other security service providers have handled this issue or what is best practice

To plan for least privilege, indexes would be separated out by group. We could store all data related to the group in a respective index. Network traffic, network security, antivirus, Windows Event Data, etc all in a single index for the group and give that group permissions to the index.

An issue with this scenario is search performance. Searches may be performed on network traffic. Or on host data. Or on antivirus data. But Splunk will have to search the bucket containing all of the other unrelated data. If antivirus data is only producing 5GB a day, but network traffic is producing 10TB a day, this will have a huge negative affect on searches for antivirus data. This will be compounded with SmartStore (S2) where IOPS will be used to write the bucket back to disk.

If least privilege isn't an issue, it would be optimal to create a bucket for the specific data. Network traffic would have it's own index. Windows hosts would have their own index. But the crux of architecting in this fashion is how to implement least privilege. One group cannot be able to see the host data of the other group. One idea to get around this is to limit the search capability by host, but that would require much work from the Splunk team and is not 100% certain if wildcards are used.

Another idea is to simply create a separate index for each data type for each group. My concern with this is scaling. If we have 10 separate groups that require 10 indexes, that's 100 indexes. If we have 50, that's 500 indexes. 100 is 1000. This does not scale well.

Thank you in advance for your help

[datadevops]

Hi there,

Here's my take on your query:

Least Privilege vs. Performance:

• Separate indexes: While ideal for least privilege, searching across multiple indexes impacts performance, especially with SmartStore's IOPS usage.

• Single index with access controls: Offers better performance but weakens least privilege. You could use ACLs or user roles to restrict data access within an index.

Balancing Act:

• Data classification: Classify data into security levels (highly sensitive, sensitive, general). Implement least privilege based on these levels.

• Hybrid approach: Use separate indexes for highly sensitive data and combine lower-sensitivity data from multiple groups into a single, access-controlled index.

• Search optimization: Tune searches to target specific indexes and data types. Utilize summary indexes or distributed searches for broader queries.

Scaling with Groups:

• Index replication: Replicate relevant data subsets to separate indexes for specific groups. This balances access control with performance.

• Splunk User Conductors: Leverages a central team to manage group data access and conduct privileged searches when needed.

• Invest in Splunk expertise: Consider consulting Splunk specialists for guidance on architecting a scalable and secure solution.

Remember:

• There's no one-size-fits-all solution. Evaluate your specific security needs, data volume, and search requirements.

• Prioritize data security without sacrificing performance entirely. Find the right balance through a combination of strategies.

• Leverage Splunk documentation and community resources for best practices and expert insights.

Balancing least privilege with search performance is a common challenge in Splunk security setups. Here's my take on your query:

Least Privilege vs. Performance:

• Separate indexes: While ideal for least privilege, searching across multiple indexes impacts performance, especially with SmartStore's IOPS usage.

• Single index with access controls: Offers better performance but weakens least privilege. You could use ACLs or user roles to restrict data access within an index.

Balancing Act:

• Data classification: Classify data into security levels (highly sensitive, sensitive, general). Implement least privilege based on these levels.

• Hybrid approach: Use separate indexes for highly sensitive data and combine lower-sensitivity data from multiple groups into a single, access-controlled index.

• Search optimization: Tune searches to target specific indexes and data types. Utilize summary indexes or distributed searches for broader queries.

Scaling with Groups:

• Index replication: Replicate relevant data subsets to separate indexes for specific groups. This balances access control with performance.

• Splunk User Conductors: Leverages a central team to manage group data access and conduct privileged searches when needed.

• Invest in Splunk expertise: Consider consulting Splunk specialists for guidance on architecting a scalable and secure solution.

Remember:

• There's no one-size-fits-all solution. Evaluate your specific security needs, data volume, and search requirements.

• Prioritize data security without sacrificing performance entirely. Find the right balance through a combination of strategies.

• Leverage Splunk documentation and community resources for best practices and expert insights.

~ If the reply helps, a Karma upvote would be appreciated