To facilitate team member collaboration, your organization uses cloud-based code collaboration and version control for sharing computer program source code and associated documentation. This system allows for sharing but also introduces the potential of intellectual property loss via data exfiltration through a systems breach or insider threat. You need some fundamental procedures for detecting behavior that could be indicative of data exfiltration or any other security risk to your source code.
How to use Splunk software for this use case
You can use Splunk software to monitor who accesses specific GitHub repositories, what actions they take in those repositories, and how their activities compare to those of their peers. You can identify first-time access to repos and compare what is accessed with the role and responsibilities of the identity making the access. Finally, you can use Splunk software for statistical analyses like frequency, patterns of access, and time of day information.
This use case is best deployed using Splunk Security Essentials (SSE), a free application with a security content library. However, you can run these first two searches on any Splunk Enterprise or Splunk Cloud Platform deployment.
These additional searches use macros that come packaged with the Splunk Security Essentials application. You will not be able to run these searches as written if you have not installed Splunk Security Essentials.
To maximize their benefit, the searches above likely need to tie into existing processes at your organization or become new standard processes. These processes commonly impact success with this use case:
- Identity management with roles, responsibilities, teams, and current project assignments to aid in identifying anomalous access from inside.
Measuring impact and benefit is critical to assessing the value of IT operations. The following are example metrics that can be useful to monitor when implementing this use case:
- Access counts by user to detect anomalous patterns
- Count of downloads to detect anomalous increases