A Splunk administrator needs to integrate a third-party vulnerability management tool to automate
remediation workflows.
What is the most efficient first step?
B
Explanation:
Why Use REST APIs for Integration?
When integrating a third-party vulnerability management tool (e.g., Tenable, Qualys, Rapid7) with
Splunk SOAR, using REST APIs is the most efficient and scalable approach.
Why REST APIs?
APIs enable direct communication between Splunk SOAR and the third-party tool.
Allows automated ingestion of vulnerability data into Splunk.
Supports automated remediation workflows (e.g., patch deployment, firewall rule updates).
Reduces manual work by allowing Splunk SOAR to pull real-time data from the vulnerability tool.
Steps to Integrate a Third-Party Vulnerability Tool with Splunk SOAR Using REST API:
️
⃣
Obtain API Credentials – Get API keys or authentication tokens from the vulnerability
management tool.
️
⃣
Configure REST API Integration – Use Splunk SOAR’s built-in API connectors or create a custom
REST API call.
️
⃣
Ingest Vulnerability Data into Splunk – Map API responses to Splunk ES correlation searches.
️
⃣
Automate Remediation Playbooks – Build Splunk SOAR playbooks to:
Automatically open tickets for critical vulnerabilities.
Trigger patches or firewall rules for high-risk vulnerabilities.
Notify SOC analysts when a high-risk vulnerability is detected on a critical asset.
Example Use Case in Splunk SOAR:
Scenario: The company uses Tenable.io for vulnerability management.
✅
Splunk SOAR connects to Tenable’s API and pulls vulnerability scan results.
✅
If a critical vulnerability is found on a production server, Splunk SOAR:
Automatically creates a ServiceNow ticket for remediation.
Triggers a patching script to fix the vulnerability.
Updates Splunk ES dashboards for tracking.
Why Not the Other Options?
❌
A. Set up a manual alerting system for vulnerabilities – Manual alerting is inefficient and doesn’t
scale well.
❌
C. Write a correlation search for each vulnerability type – This would create too many rules; API
integration allows real-time updates from the vulnerability tool.
❌
D. Configure custom dashboards to monitor vulnerabilities – Dashboards provide visibility but
don’t automate remediation.
Reference & Learning Resources
Splunk SOAR API Integration Guide: https://docs.splunk.com/Documentation/SOAR
Integrating Tenable, Qualys, Rapid7 with Splunk: https://splunkbase.splunk.com
REST API Automation in Splunk SOAR: https://www.splunk.com/en_us/products/soar.html
Which sourcetype configurations affect data ingestion? (Choose three)
A, B, D
Explanation:
The sourcetype in Splunk defines how incoming machine data is interpreted, structured, and stored.
Proper sourcetype configurations ensure accurate event parsing, indexing, and searching.
✅
1. Event Breaking Rules (A)
Determines how Splunk splits raw logs into individual events.
If misconfigured, a single event may be broken into multiple fragments or multiple log lines may be
combined incorrectly.
Controlled using LINE_BREAKER and BREAK_ONLY_BEFORE settings.
✅
2. Timestamp Extraction (B)
Extracts and assigns timestamps to events during ingestion.
Incorrect timestamp configuration leads to misplaced events in time-based searches.
Uses TIME_PREFIX, MAX_TIMESTAMP_LOOKAHEAD, and TIME_FORMAT settings.
✅
3. Line Merging Rules (D)
Controls whether multiline events should be combined into a single event.
Useful for logs like stack traces or multi-line syslog messages.
Uses SHOULD_LINEMERGE and LINE_BREAKER settings.
❌
Incorrect Answer:
C . Data Retention Policies →
Affects storage and deletion, not data ingestion itself.
Additional Resources:
Splunk Sourcetype Configuration Guide
Event Breaking and Line Merging
What is a key feature of effective security reports for stakeholders?
A
Explanation:
Security reports provide stakeholders (executives, compliance officers, and security teams) with
insights into security posture, risks, and recommendations.
✅
Key Features of Effective Security Reports
High-Level Summaries
Stakeholders don’t need raw logs but require summary-level insights on threats and trends.
Actionable Insights
Reports should provide clear recommendations on mitigating risks.
Visual Dashboards & Metrics
Charts, KPIs, and trends enhance understanding for non-technical stakeholders.
❌
Incorrect Answers:
B . Detailed event logs for every incident → Logs are useful for analysts, not executives.
C . Exclusively technical details for IT teams → Reports should balance technical & business insights.
D . Excluding compliance-related metrics → Compliance is critical in security reporting.
Additional Resources:
Splunk Security Reporting Best Practices
Creating Executive Security Reports
Which Splunk feature enables integration with third-party tools for automated response actions?
B
Explanation:
Security teams use Splunk Enterprise Security (ES) and Splunk SOAR to integrate with firewalls,
endpoint security, and SIEM tools for automated threat response.
✅
Workflow Actions (B) - Key Integration Feature
Allows analysts to trigger automated actions directly from Splunk searches and dashboards.
Can integrate with SOAR playbooks, ticketing systems (e.g., ServiceNow), or firewalls to take action.
Example:
Block an IP on a firewall from a Splunk dashboard.
Trigger a SOAR playbook for automated threat containment.
❌
Incorrect Answers:
A . Data Model Acceleration → Speeds up searches, but doesn’t handle integrations.
C . Summary Indexing → Stores summarized data for reporting, not automation.
D . Event Sampling → Reduces search load, but doesn’t trigger automated actions.
Additional Resources:
Splunk Workflow Actions Documentation
Automating Response with Splunk SOAR
Which action improves the effectiveness of notable events in Enterprise Security?
A
Explanation:
Notable events in Splunk Enterprise Security (ES) are triggered by correlation searches, which
generate alerts when suspicious activity is detected. However, if too many false positives occur,
analysts waste time investigating non-issues, reducing SOC efficiency.
How to Improve Notable Events Effectiveness:
Apply suppression rules to filter out known false positives and reduce alert fatigue.
Refine correlation searches by adjusting thresholds and tuning event detection logic.
Leverage risk-based alerting (RBA) to prioritize high-risk events.
Use adaptive response actions to enrich events dynamically.
By suppressing false positives, SOC analysts focus on real threats, making notable events more
actionable. Thus, the correct answer is A. Applying suppression rules for false positives.
Reference:
Managing Notable Events in Splunk ES
Best Practices for Tuning Correlation Searches
Using Suppression in Splunk ES
Which actions can optimize case management in Splunk? (Choose two)
AC
Explanation:
Effective case management in Splunk Enterprise Security (ES) helps streamline incident tracking,
investigation, and resolution.
How to Optimize Case Management:
Standardizing ticket creation workflows (A)
Ensures consistency in how incidents are reported and tracked.
Reduces manual errors and improves collaboration between SOC teams.
Integrating Splunk with ITSM tools (C)
Automates the process of creating and updating tickets in ServiceNow, Jira, or Remedy.
Enables better tracking of incidents and response actions.
Incorrect Answers:
❌
B. Increasing the indexing frequency – This improves data availability but does not directly
optimize case management.
❌
D. Reducing the number of search heads – This might degrade search performance rather than
optimize case handling.
Reference:
Splunk ES Case Management
Integrating Splunk with ServiceNow
Automating Ticket Creation in Splunk
Which REST API actions can Splunk perform to optimize automation workflows? (Choose two)
A, C
Explanation:
The Splunk REST API allows programmatic access to Splunk’s features, helping automate security
workflows in a Security Operations Center (SOC).
Key REST API Actions for Automation:
POST for creating new data entries (A)
Used to send logs, alerts, or notable events to Splunk.
Essential for integrating external security tools with Splunk.
GET for retrieving search results (C)
Fetches logs, alerts, and notable event details programmatically.
Helps automate security monitoring and incident response.
Incorrect Answers:
❌
B. DELETE for archiving historical data – DELETE is rarely used in Splunk as it does not archive
data; instead, retention policies handle old data.
❌
D. PUT for updating index configurations – While PUT can modify configurations, it is not a core
automation function in SOC workflows.
Reference:
Splunk REST API Documentation
Using Splunk API for Automation
Best Practices for Automating Security Workflows
What is the main purpose of Splunk's Common Information Model (CIM)?
B
Explanation:
What is the Splunk Common Information Model (CIM)?
Splunk’s Common Information Model (CIM) is a standardized way to normalize and map event data
from different sources to a common field format. It helps with:
Consistent searches across diverse log sources
Faster correlation of security events
Better compatibility with prebuilt dashboards, alerts, and reports
Why is Data Normalization Important?
Security teams analyze data from firewalls, IDS/IPS, endpoint logs, authentication logs, and cloud
logs.
These sources have different field names (e.g., “src_ip” vs. “source_address”).
CIM ensures a standardized format, so correlation searches work seamlessly across different log
sources.
How CIM Works in Splunk?
✅
Maps event fields to a standardized schema
✅
Supports prebuilt Splunk apps like Enterprise Security (ES)
✅
Helps SOC teams quickly detect security threats
Example Use Case:
A security analyst wants to detect failed admin logins across multiple authentication systems.
Without CIM, different logs might use:
user_login_failed
auth_failure
login_error
With CIM, all these fields map to the same normalized schema, enabling one unified search query.
Why Not the Other Options?
❌
A. Extract fields from raw events – CIM does not extract fields; it maps existing fields into a
standardized format.
❌
C. Compress data during indexing – CIM is about data normalization, not compression.
❌
D. Create accelerated reports – While CIM supports acceleration, its main function is
standardizing log formats.
Reference & Learning Resources
Splunk CIM Documentation: https://docs.splunk.com/Documentation/CIM
How Splunk CIM Helps with Security Analytics: https://www.splunk.com/en_us/solutions/common-information-model.html
Splunk Enterprise Security & CIM Integration: https://splunkbase.splunk.com/app/263
A company’s Splunk setup processes logs from multiple sources with inconsistent field naming
conventions.
How should the engineer ensure uniformity across data for better analysis?
C
Explanation:
Why Use CIM for Field Normalization?
When processing logs from multiple sources with inconsistent field names, the best way to ensure
uniformity is to use Splunk’s Common Information Model (CIM).
Key Benefits of CIM for Normalization:
Ensures that different field names (e.g., src_ip, ip_src, source_address) are mapped to a common
schema.
Allows security teams to run a single search query across multiple sources without manual mapping.
Enables correlation searches in Splunk Enterprise Security (ES) for better threat detection.
Example Scenario in a SOC:
Problem: The SOC team needs to correlate firewall logs, cloud logs, and endpoint logs for failed
logins.
✅
Without CIM: Each log source uses a different field name for failed logins, requiring multiple
search queries.
✅
With CIM: All failed login events map to the same standardized field (e.g., action="failure"),
allowing one unified search query.
Why Not the Other Options?
❌
A. Create field extraction rules at search time – Helps with parsing data but doesn’t standardize
field names across sources.
❌
B. Use data model acceleration for real-time searches – Accelerates searches but doesn’t fix
inconsistent field naming.
❌
D. Configure index-time data transformations – Changes fields at indexing but is less flexible than
CIM’s search-time normalization.
Reference & Learning Resources
Splunk CIM for Normalization: https://docs.splunk.com/Documentation/CIM
Splunk ES CIM Field Mappings: https://splunkbase.splunk.com/app/263
Best Practices for Log Normalization: https://www.splunk.com/en_us/blog/tips-and-tricks
Which Splunk configuration ensures events are parsed and indexed only once for optimal storage?
C
Explanation:
Why Use Index-Time Transformations for One-Time Parsing & Indexing?
Splunk parses and indexes data once during ingestion to ensure efficient storage and search
performance. Index-time transformations ensure that logs are:
✅
Parsed, transformed, and stored efficiently before indexing.
✅
Normalized before indexing, so the SOC team doesn’t need to clean up fields later.
✅
Processed once, ensuring optimal storage utilization.
Example of Index-Time Transformation in Splunk:
Scenario: The SOC team needs to mask sensitive data in security logs before storing them in Splunk.
✅
Solution: Use an INDEXED_EXTRACTIONS rule to:
Redact confidential fields (e.g., obfuscate Social Security Numbers in logs).
Rename fields for consistency before indexing.