Pass the Splunk Cybersecurity Defense Analyst SPLK-5002 Questions and answers with CertsForce

Correlation searches in Splunk Enterprise Security (ES) are a critical component of Security Operations Center (SOC) workflows, designed to detect threats by analyzing security data from multiple sources.

Primary Purpose of Correlation Searches:

Identify threats and anomalies: They detect patterns and suspicious activity by correlating logs, alerts, and events from different sources.

Automate security monitoring: By continuously running searches on ingested data, correlationsearches help reduce manual efforts for SOC analysts.

Generate notable events: When a correlation search identifies a security risk, it creates a notable event in Splunk ES for investigation.

Trigger security automation: In combination with Splunk SOAR, correlation searches can initiate automated response actions, such as isolating endpoints or blocking malicious IPs.

Since correlation searches analyze relationships and patterns across multiple data sources to detect security threats, the correct answer is B. To identify patterns and relationships between multiple data sources.

References:

Splunk ES Correlation Searches Overview

Best Practices for Correlation Searches

Splunk ES Use Cases and Notable Events

Risk-based detection in Splunk prioritizes alerts based on behavior, threat intelligence, and business impact. Enhancing risk scores and enriching contextual data ensures that SOC teams focus on the most critical threats.

Methods to Enhance Risk-Based Detection:

Defining Accurate Risk Modifiers (A)

Adjusts risk scores dynamically based on asset value, user behavior, and historical activity.

Ensures that low-priority noise doesn’t overwhelm SOC analysts.

Enriching Risk Objects with Contextual Data (D)

Adds threat intelligence feeds, asset criticality, and user behavior data to alerts.

Improves incident triage and correlation of multiple low-level events into significant threats.

How Splunk SOAR Improves Phishing Response?

Phishing attacks require fast detection and response. Manual investigation delays can be eliminated using Splunk SOAR automation.

????Why Use Playbooks for Automated Email Triage? (Answer B)✅Extracts email headers and attachments for analysis✅Checks links & attachments against threat intelligence feeds✅Automatically quarantines or deletes malicious emails✅Escalates high-risk cases to SOC analysts

????Example Playbook Workflow in Splunk SOAR:????Scenario: A suspicious email is reported.✅Splunk SOAR playbook automatically:

Extracts sender details & checks against threat intelligence

Analyzes URLs & attachments using VirusTotal/Sandboxing

Tags the email as "Malicious" or "Safe"

Quarantines the email & alerts SOC analysts

Why Not the Other Options?

❌A. Prioritizing phishing cases manually – Still requires manual effort, leading to delays.❌C. Assigning cases to analysts in real-time – Doesn’t solve the issue of slow manual investigations.❌D. Increasing the indexing frequency of email logs – Helps with log retrieval but doesn’t automate phishing response.

References & Learning Resources

????Splunk SOAR Phishing Playbook Guide: https://docs.splunk.com/Documentation/SOAR ????Phishing Detection Automation in Splunk: https://splunkbase.splunk.com ????Email Threat Intelligence with SOAR: https://www.splunk.com/en_us/blog/security

When organizations need to normalize event data from various sources, using Common Information Model (CIM) in Splunk is the best approach.

Why Use CIM for Normalized Event Data?

Standardizes Data Across Different Log Sources

CIM ensures consistent field names and formats across varied log types.

Makes searches, reports, and dashboards easier to manage.

Enables Faster and More Efficient Searches

Uses Data Models to accelerate search queries.

Reduces the need for custom field extractions.

Security metrics help organizations assess their security posture and make data-driven decisions.

Primary Purpose of Security Metrics in Splunk:

Measure Security Effectiveness (B)

Tracks incident response times, threat detection rates, and alert accuracy.

Helps SOC teams and leadership evaluate security program performance.

Improve Threat Detection & Incident Response

Identifies gaps in detection logic and false positives.

Helps fine-tune correlation searches and notable events.

The Splunk Data Pipeline consists of multiple stages that process incoming data from ingestion to visualization.

Main Steps of the Splunk Data Pipeline:

Input Phase (C)

Splunk collects raw data from logs, applications, network traffic, and endpoints.

Supports various data sources like syslog, APIs, cloud services, and agents (e.g., Universal Forwarders).

Parsing (D)

Splunk breaks incoming data into events and extracts metadata fields.

Removes duplicates, formats timestamps, and applies transformations.

Indexing (A)

Stores parsed events into indexes for efficient searching.

Supports data retention policies, compression, and search optimization.

Configurations Required for Data Normalization in Splunk

Data normalization ensures consistent field naming and event structuring, especially for Splunk Common Information Model (CIM) compliance.

✅1. props.conf (A)

Defines how data is parsed and indexed.

Controls field extractions, event breaking, and timestamp recognition.

Example:

Assigns custom sourcetypes and defines regex-based field extraction.

✅2. transforms.conf (B)

Used for data transformation, lookup table mapping, and field aliasing.

Example:

Normalizes firewall logs by renaming src_ip → src to align with CIM.

❌Incorrect Answers:

C. savedsearches.conf → Defines scheduled searches, not data normalization.

D. authorize.conf → Manages user permissions, not data normalization.

E. eventtypes.conf → Groups events into categories but doesn’t modify data structure.

????Additional Resources:

Splunk Data Normalization Guide

Understanding props.conf and transforms.conf

Monitoring indexing performance in Splunk is crucial for ensuring efficient data ingestion, search performance, and resource utilization.

Methods to Monitor Indexing Performance Effectively:

Use the Monitoring Console (A)

Provides real-time visibility into indexing performance.

Displays resource utilization, indexing rate, queue health, and disk usage.

Track Indexer Queue Size and Throughput (D)

Monitoring queue sizes prevents indexing bottlenecks.

Ensures data is processed efficiently without delays.

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.

References:

Managing Notable Events in Splunk ES

Best Practices for Tuning Correlation Searches

Using Suppression in Splunk ES

Methods to Improve Dashboard Usability in Security Analytics

A well-designed Splunk security dashboard helps SOC teams quickly identify, analyze, and respond to security threats.

✅1. Using Drill-Down Options for Detailed Views (A)

Allows analysts to click on high-level metrics and drill down into event details.

Helps teams pivot from summary statistics to specific security logs.

Example:

Clicking on a failed login trend chart reveals specific failed login attempts per user.

✅2. Standardizing Color Coding for Alerts (B)

Consistent color usage enhances readability and priority identification.

Example:

Red → Critical incidents

Yellow → Medium-risk alerts

Green → Resolved issues

✅3. Adding Context-Sensitive Filters (D)

Filters allow users to focus on specific security events without running new searches.

Example:

A dropdown filter for "Event Severity" lets analysts view only high-risk events.

❌Incorrect Answers:

C. Limiting the number of panels on the dashboard → Dashboards should be optimized, not restricted.

E. Avoiding performance optimization → Performance tuning is essential for responsive dashboards.

????Additional Resources:

Splunk Dashboard Design Best Practices

Optimizing Security Dashboards in Splunk