Establishing disaster recovery and business continuity

Evaluating your Splunk environment

The Splunk platform integrates with a variety of data types ranging from security logs to system performance metrics. Understanding these sources and their significance is vital for effective disaster recovery planning.

Designing the disaster recovery plan

Updating the DRP as the Splunk environment evolves

Your Splunk deployment will change over time. Any environment changes approved via the customer change control system should include a step to check that no modifications are needed for the DRP itself.  New data sources might be added; others might become obsolete. As you scale or restructure, ensure your DRP reflects these changes. Review and update the DRP at least annually or after significant infrastructure changes, such as:

• Changes in Data Sources: As your organization grows and evolves, you're likely to add new data sources to your Splunk environment. These could include new applications, servers, cloud services, or even IoT devices. To ensure that your DRP remains effective, it's important to incorporate these new data sources into your recovery plan. This means identifying how these sources will be backed up, restored, or replicated in the event of a disaster.
• Obsolete Data Sources: Conversely, some data sources that were once critical might become obsolete or less important over time. When this happens, it's important to update your DRP to reflect these changes. This might involve removing references to outdated data sources or adjusting recovery priorities to focus on the most critical systems and data.
• Scaling or Restructuring: Organizations often undergo changes in their infrastructure, such as scaling up to accommodate increased data volumes or restructuring to improve efficiency. These changes can impact your DRP, as the processes and procedures for disaster recovery might need to be adapted to suit the new infrastructure. Regularly reviewing and updating your DRP ensures that it remains in sync with your infrastructure's current state.
• Significant Infrastructure Changes: In addition to the annual review, update your DRP promptly after any significant infrastructure changes. These changes could include major system upgrades, data center relocations, or other strategic decisions that impact the configuration of your Splunk environment. Prompt updates ensure that your DRP remains accurate and reliable in the face of sudden disruptions.

Helpful resources

Prerequisite knowledge

Before you begin working on this section, you should be familiar with the concepts in the following articles from Splunk documentation:

• The basics of indexer cluster architecture  
• Multisite indexer cluster architecture  
• How clustered indexing works

Questions to answer before beginning backup

1. Business Continuity Objectives:
   • What are your recovery time objectives (RTO) and recovery point objectives (RPO) for your Splunk data?
2. Splunk Infrastructure:
   • How is your Splunk deployment structured? Are you utilizing clustered features, such as indexer clustering or search head clustering?
   • How many indexers are in your Splunk deployment, and what is their average data ingest rate?
   • Are you using Splunk Enterprise Security or Splunk ITSI?
3. Current Backup Mechanism:
   • Do you have an existing backup mechanism in place? If so, what are its capabilities and limitations?
   • How frequently do you currently perform backups of your critical systems?
4. Operational Constraints:
   • Are there specific time windows when backups must (or must not) occur due to operational needs or system loads?
   • Are there any bandwidth or resource constraints to be aware of when planning backup strategies?
5. Data Retention and Compliance:
   • Are there specific data retention requirements or policies that your organization adheres to?
   • Do you have any industry or regulatory compliance standards (for example, GDPR, HIPAA) that affect how data is backed up and retained?
6. Disaster Recovery Scenarios:
   • Have you identified specific disaster scenarios (for example, data corruption, server failure, data center outages) you want to protect against?
   • Have you ever had to restore Splunk data in the past? If so, what was that experience like?
7. Budget and Resource Allocation:
   • Do you have a dedicated budget for backup and disaster recovery solutions?
   • What internal or external resources (personnel, hardware, software) are available or allocated for backup and disaster recovery efforts?
8. Data Integrity and Validation:
   • How will you validate the integrity of backups? Do you need to perform regular test restores?
   • Do you have mechanisms or processes in place to monitor and alert on backup failures?
9. Geographic Considerations:
   • Is geographic redundancy necessary for your backup strategy (for example, backing up data to a different region or data center)?
10. Integration with Other Systems:
    • Are other systems or data sources interdependent with your Splunk data, which might need to be considered in a coordinated backup or restore strategy?

Built-in replication compared to additional backups

While the native replication mechanisms, which are the Replication Factor (RF) and Search Factor (SF), offer real-time data resilience, traditional backups provide a safety net against data corruption, failures, or other significant disruptions. As we get deeper into this section, we'll contrast the proactive, immediate recovery benefits of built-in replication in the Splunk platform against the more retrospective, long-term data retention advantages of backups. By the end, you'll have a clear understanding of when and why to apply each method, and how they collaboratively enhance the overall resiliency of your Splunk deployment.

In the context of safeguarding Splunk index data, the primary approach should revolve around utilizing the search factor and replication factor. These built-in mechanisms provide an intrinsic layer of data protection, ensuring that indexed data is appropriately duplicated across multiple peers in the Splunk cluster. By leveraging these factors, organizations can maintain data accessibility and resilience even in the face of potential node failures or data corruption scenarios. Before considering additional backup strategies, it's imperative to set and optimize these factors according to the specific needs and infrastructure of the deployment, as they serve as the foundational pillars of data reliability within the Splunk platform.

Reference the Splunk Validated Architectures for assistance in designing the Splunk deployment that will meet your organization's backup and recovery goals.

Challenges and considerations with traditional backup strategies for indexes

While traditional backups can provide a snapshot of your Splunk data at a particular point in time, there are inherent risks associated with this method. These include the following:

• The dynamic nature of Splunk data means that backups can quickly become outdated, potentially leading to data gaps in the event of a restore.
• Restoring the Splunk platform from a conventional backup can be time-consuming, potentially leading to prolonged service disruptions.
• The restoration process might not always guarantee the integrity of the indexed data, especially if the backup was taken during heavy indexing or searching operations.

As such, while traditional backups can serve as a supplementary layer of protection, they should not replace the native resiliency provided by the proper configuration of the search factor and replication factor in the Splunk platform. Before embarking on a traditional backup strategy, you should weigh the benefits for each index type against these potential challenges to make informed decisions.

• Hot Indexes:
  • Challenges: Hot indexes are the active indexes, where new data is continuously being written. They can be volatile and are often locked, making them more challenging to backup.
  • Considerations: Due to their active nature, it is typically recommended to avoid direct backups of hot indexes. Instead, rely on Splunk's inherent replication capabilities to ensure data durability.
• Warm Indexes:
  • Challenges: While not as volatile as hot indexes, warm indexes still see significant read activity. They are rotated data sets are no longer written to, and therefore can be backed up effectively via traditional backup solutions, but are still readily searchable.
  • Considerations: Periodic snapshots of warm indexes, especially for critical data, can be a good idea.
• Cold Indexes:
  • Challenges: Cold indexes are older, archived data that has been rolled out from the warm phase, typically onto slower, less expensive storage. Their sheer size can make backups lengthy and storage-intensive.
  • Considerations: Given that cold indexes are stable (no new data is written here), it's feasible to employ traditional backup methods. A complete backup of all cold indexes ensures a safeguard against any unforeseen data loss. Additionally, consider storage solutions that are both cost-effective and reliable for these larger backups.
• Thawed and Frozen Indexes: While not part of the standard data lifecycle in the same way, it's worth noting thawed and frozen indexes when discussing backup strategies.
  • Thawed Indexes: These are resurrected cold indexes, brought back for specific reasons, like a particular investigation or analysis.
  • Frozen Indexes: Data in this state is effectively considered disposable by the Splunk platform. Before data transitions to the frozen state, ensure you've made the necessary backups if retention is required.

When crafting a traditional backup strategy, you should understand the distinct characteristics and challenges associated with each index state. It informs not only the backup methods employed but also the frequency, storage considerations, and recovery strategies to ensure the optimal safeguarding of your Splunk data.

Configuration backup

Splunk configurations dictate how the platform behaves, ingests and processes data, and visualizes your data. If these configurations are lost or corrupted, you might end up with disrupted services, data ingestion issues, or inaccurate insights. Consider scheduled backups to ensure resilience for your core configurations. These backups should be stored in secure and redundant storage solutions. Don’t just trust that your backups happened, even if the jobs show as successful. Periodically validate the backups and ensure integrity and completeness.

Helpful resources

• Splunk Docs: The basics of indexer cluster architecture 
• Splunk Docs: Multisite indexer cluster architecture 
• Splunk Docs: How clustered indexing works 
• Data Descriptor: Backup data 
• Data Descriptor: GitLab
• Product Tip: Automating Splunk platform administration with a Continuous Configuration Automation framework 
• Splunk Success Framework: Managing backup and restore processes 
• Splunk Success Framework: Preparing for failures in the Splunk utility tier 

Implications of data loss or service disruptions

Any disruption to Splunk services or loss of data can have far-reaching consequences, such as the following:

• Operational Disruption: Real-time monitoring and alerting can get hampered, leaving systems vulnerable to undetected issues.
• Regulatory Repercussions: Non-compliance due to missing logs can result in penalties, audits, or legal ramifications.
• Loss of Trust: Data breaches or loss can erode stakeholder and customer trust, potentially leading to reputational damage.

Value of a disaster recovery procedure

A robust DRP provides the following benefits:

• Quick Recovery: A step-by-step guide to recover services swiftly, minimizing downtime.
• Data Integrity: The DRP ensures not just data recovery but also its integrity, ensuring that restored data is consistent and uncorrupted.
• Service Continuity: Even in the face of disasters, critical Splunk services, such as real-time monitoring, alerting, and reporting, continue with minimal disruptions.

Importance of regular DRP testing

• Validation of Procedures: To gauge the practicality and efficiency of the delineated steps in the DRP. Given the complexity and dynamic nature of Splunk deployments, it's important to ensure that the recovery steps align with the latest configurations and are adaptable to evolving data architectures. Regular testing certifies that the DRP remains relevant and actionable, mitigating potential data loss or extended service outages.
• Skill Familiarity: To maintain a high level of proficiency and readiness among the team members responsible for disaster recovery. The typical Splunk environment - comprising various indexers, search heads, and forwarders - requires specific expertise for efficient restoration. Regular DRP drills reinforce familiarity with Splunk-specific recovery tasks, ensuring a swift and informed response during actual disruptions, thereby minimizing downtime.
• Infrastructure Assessment: To identify and rectify potential infrastructure challenges that could compromise a successful recovery. By conducting test recoveries, organizations can pinpoint bottlenecks or vulnerabilities within their Splunk deployment, such as inadequate storage capacity or network constraints. Addressing these proactively ensures uninterrupted access to critical data insights even post-recovery.

Helpful resources

• Splunk Blog: Disaster recovery planning: The organizational guide 

Define key metrics for monitoring

Before implementing monitoring and alerting, identify the critical metrics that need monitoring. Focus on system health (CPU, memory, disk space), network connectivity, and data replication status. Understanding your organization's specific needs will help you choose the right metrics to track and ensure proactive monitoring.

1. Assess Critical Splunk Systems and Services: Start by assessing the critical systems: indexers, search heads, license managers, deployment server, services, and applications essential to your organization's Splunk deployment. This might include servers, networking equipment, cloud resources, and other critical services. Identify the dependencies between these components to understand their interconnectivity.
2. Identify Potential Failure Points: After you clearly understand your critical systems, identify potential failure points or weak links that could lead to disruptions. For example, consider CPU and memory utilization, disk space availability, network latency, and data replication status. These metrics are crucial for detecting resource shortages or system failures.
3. Determine Acceptable Thresholds: Establish acceptable thresholds for each metric. These thresholds define the limits beyond which a metric is considered abnormal or indicative of a potential issue. For example, you might set a threshold for CPU utilization at 80%, indicating that a sustained CPU usage above this level could be a cause for concern.
4. Align Metrics with Business Objectives: Ensure that the chosen metrics align with your organization's business objectives and disaster recovery priorities. Focus on metrics that directly impact critical business processes, customer experience, and regulatory compliance. This alignment ensures that your monitoring efforts are targeted and impactful.
5. Consider Historical Data and Trends: Analyze historical data to identify patterns and trends that might indicate potential issues. Understanding historical behavior helps in setting dynamic thresholds that adapt to changing usage patterns. For instance, you might notice that disk utilization tends to spike during specific time frames, and adjusting thresholds accordingly will prevent false alerts.
6. Collaborate with Stakeholders: Involve key stakeholders, including IT teams, business owners, and disaster recovery experts, in the process of defining monitoring metrics. Gathering input from different perspectives will lead to a comprehensive and well-rounded monitoring strategy.
7. Continuously Review and Update: Monitoring requirements evolve over time due to changes in technology, business processes, and external factors. Regularly review and update your monitoring metrics to stay aligned with the organization's changing needs.

Select appropriate data sources

The Splunk platform offers various options for monitoring and setting up alerts across diverse data sources. Choose data sources that provide relevant information to monitor your disaster recovery and business continuity efforts effectively. Consider using built-in monitoring apps, along with relevant add-ons or custom data sources tailored to your requirements.

• Splunk Indexer and Search Head Status: Monitor the availability and performance of your Splunk indexers and search heads. Check for any service disruptions or issues that could affect data indexing and search capabilities. Consider checking the health endpoint for Splunk service status from the cluster manager by using | rest /services/server/health-config.
• License Usage: Keep track of your Splunk license usage, including daily indexing volume and the number of indexed events. Ensure that you do not exceed your licensed limits to prevent disruptions to data ingestion.
• Disk Space Utilization: Monitor the disk space usage on all Splunk servers, particularly on indexers, to avoid data loss due to insufficient storage. Set up alerts for low disk space conditions to take timely action.
• System Resource Utilization: Monitor CPU, memory, and network utilization on your Splunk servers. High resource usage can lead to performance degradation or even system crashes.
• Indexing Performance: Track the rate at which data is being indexed and the time it takes for events to become searchable. Monitoring indexing performance helps identify bottlenecks and optimize data ingestion.
• Search Performance: Monitor the response times of search queries. Slow searches can impact user experience and might indicate issues with system performance.
• Forwarder Status: Monitor the health of Universal Forwarders to ensure that data collection from various sources is functioning correctly.
• Splunk Internal Logs: Monitor Splunk's internal logs, including logs related to licensing, indexing, and search activities. These logs provide valuable insights into the health of the Splunk platform itself.
• Replication Status (for Distributed Environments): In distributed Splunk environments, monitor data replication across indexers and search heads to ensure data redundancy and high availability.
• Splunk Web Interface: Regularly check the accessibility and performance of the Splunk Web interface to ensure users can access and interact with the platform without interruptions.
• Health Check Dashboards: Leverage pre-built health check dashboards or design custom ones to consolidate the essential metrics mentioned earlier, using both the Monitoring Console and Cloud Monitoring Console. These dashboards offer a comprehensive overview of the overall health of the Splunk platform.

By monitoring these critical items in the Splunk platform, you can proactively identify and address any issues, optimize performance, and ensure the reliability and stability of your Splunk deployment. Regular monitoring and alerting enable quick responses to potential problems, contributing to the overall effectiveness of your Splunk environment.

Create alerting rules

Crafting well-defined alerting rules is essential for timely response to potential issues. Establish thresholds for each metric that, when breached, will trigger an alert. For instance, set alerts for disk space reaching a certain capacity or replication delays exceeding specific time frames. These rules will help you detect issues early and take appropriate actions swiftly. A good starting point is the built-in Monitoring Console, which you can learn about in Monitoring Splunk Enterprise overview.

Set up PICERL-based escalation procedures

PICERL is an acronym for a structured incident response process that encompasses six phases. These phases guide how organizations respond to and recover from security incidents. The stages are:

1. Preparation: This involves setting up and maintaining an incident response capability. It includes establishing an incident response policy, setting up an incident response team (IRT), creating a set of guidelines for the IRT, and ensuring that all members are trained and equipped with the necessary tools and resources.
2. Identification: This is the stage where potential security incidents are detected and acknowledged. Tools and systems can flag unusual activities, which then need to be analyzed to determine if they represent a genuine security incident.
3. Containment: After an incident is identified, you must contain the damage. Containment is often divided into two phases:
   • Short-term Containment: Immediate actions to temporarily halt the threat (for example, disconnecting a compromised system from the network).
   • Long-term Containment: More permanent measures to ensure the threat does not spread or recur.
4. Eradication: After containment, the root cause of the incident is found and completely removed from the environment. This could involve patching software, removing malicious code, or strengthening security measures.
5. Recovery: This stage is about restoring and validating system functionality for business operations to resume. It might involve restoring systems from backups, validating the integrity of system data, or ensuring that systems are free from any remnants of threats.
6. Lessons Learned: After handling the incident, teams review the incident, the effectiveness of the response, and the lessons learned. This review helps improve the incident response process for future incidents. Feedback from this phase might loop back to the preparation phase to refine and improve procedures and training.

Organizations that adopt the PICERL model are better prepared to handle incidents systematically, ensuring they are both reactive to current threats and proactive in preventing future incidents. Utilizing the PICERL response methodology for alerts in the Splunk platform ensures a systematic approach to handling and mitigating threats in real-time.

Here's a breakdown of how PICERL can be integrated into your Splunk environment:

Test your monitoring and alerting setup

Conduct mock disaster recovery drills and test scenarios to simulate potential issues. Evaluate how your system responds to alerts and fine tune the setup if necessary. Here's a detailed procedure for testing your alerting setup in the Splunk platform:

1. Test Alert Conditions: Verify that your alert conditions are set correctly and capture the desired events. Run test queries or searches against sample data to ensure that the alert conditions accurately match the events you want to monitor.
2. Use Test Data: Create test data or use synthetic events that mimic real-world scenarios. This allows you to trigger alerts without impacting your production data or systems. Ensure that the test data includes a mix of scenarios covering different severity levels.
3. Disable Real Notifications: Before starting the testing, disable any real notifications or actions that could be triggered by the alerts. This prevents unnecessary escalations or actions during the testing phase.
4. Trigger Test Alerts: After you have your test data and disabled real notifications, manually trigger the test alerts. This can be done by generating events that match the alert conditions you've set up.
5. Verify Alert Triggering: Check the Splunk system to confirm that the test alerts were triggered correctly. Validate that the triggered alerts are appearing in the Splunk interface and are listed as triggered in the alert management section.
6. Review Alert Content: Examine the details of the triggered alerts to ensure that the information included in the alerts is relevant and provides sufficient context for further investigation.
7. Check Notifications: If you have configured email or other notification methods, verify that the alerts are being sent to the designated recipients. Confirm that the notification content is clear and includes essential information for immediate action.
8. Test Escalation: If your alerting setup includes escalation procedures, simulate scenarios where alerts should escalate to higher-level personnel. Verify that the escalation process functions as intended and that alerts reach the appropriate individuals within the defined response time targets.
9. Assess Response Actions: If the alerting setup triggers any automated response actions, such as restarting services or running scripts, evaluate whether these actions run correctly and have the outcome you want.
10. Review Logs and Reports: Analyze the logs and reports generated during the testing to identify any errors or issues. Address any problems and make necessary adjustments to the alerting configurations.
11. Document the Results: Document the results of the testing, including the alerts triggered, notifications sent, and any issues identified. Use this documentation to make improvements and adjustments to your alerting setup.

Monitor performance and optimize

After implementing the monitoring and alerting system, continuously monitor its performance and effectiveness. Regularly review the alerting rules and adjust them based on changing business needs and evolving IT environments. Optimize your setup to ensure it remains relevant and efficient. Here's a detailed explanation of monitoring performance and optimizing the setup:

• Continuous Monitoring: Regularly monitor the alerting system to ensure it is operational and capturing events as expected. Keep track of the number of alerts triggered, their frequency, and their severity levels. Monitoring helps you identify any irregularities or potential issues with the alerting rules and system.
• Performance Metrics: Define and track key performance metrics for your alerting setup. Measure the time taken for alerts to trigger, the time taken for notifications to be sent, and the time taken for responses to occur. Performance metrics provide insights into the responsiveness and efficiency of the alerting system.
• Review Alerting Rules: Conduct periodic reviews of the alerting rules to assess their relevance and effectiveness. Ensure that the alert conditions still align with your business needs and IT environment. Remove or update rules that are no longer necessary or are producing excessive false positives.
• Business Needs Alignment: Align the alerting system with changing business needs and goals. Work closely with stakeholders to understand their evolving requirements and adjust the alerting rules accordingly. This ensures that the monitoring focuses on critical areas that align with business objectives.
• IT Environment Changes: Stay aware of any changes in your IT environment, such as infrastructure updates, software upgrades, or changes in data sources. Ensure that the alerting system adapts to these changes to continue providing relevant and accurate alerts.
• Optimization Strategies: Implement optimization strategies to improve the efficiency and effectiveness of the alerting setup. This might involve refining search queries, adjusting threshold levels, or employing statistical models to reduce false positives.
• Automated Responses: Explore opportunities to automate response actions for certain alerts. Automating responses can lead to faster mitigation of issues, reducing manual intervention and minimizing downtime.
• Performance Tuning: Optimize the performance of your monitoring and alerting system by tuning hardware resources, such as memory and CPU, to handle increasing data volumes and maintain responsiveness.
• Capacity Planning: Perform capacity planning to ensure that the monitoring infrastructure can handle future growth in data and events. Anticipate resource requirements and scale the system accordingly.
• Continuous Improvement: Maintain a culture of continuous improvement for your monitoring and alerting setup. Encourage feedback from users and stakeholders to identify areas for enhancement and implement iterative improvements.
• Security Considerations: Regularly review the security measures of the monitoring system to ensure that sensitive data and configurations are protected from unauthorized access.
• Training and Education: Provide training and education to the team responsible for managing the alerting system. Ensure they are equipped with the knowledge and skills to optimize and troubleshoot the setup effectively.

By continuously monitoring and optimizing your alerting system, you can proactively address issues, ensure its alignment with changing requirements, and maintain its efficiency and relevance over time. This approach enhances the overall reliability of your disaster recovery and business continuity strategies, allowing for timely responses to potential issues and minimizing downtime.

Helpful resources