Score Higher on Verified SPLK-5002 | Splunk Certified Cybersecurity Defense Engineer Exam Questions with Answers

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

Threat intelligence in Splunk Enterprise Security (ES) enhances SOC capabilities by identifying known attack patterns, suspicious activity, and malicious indicators.

Essential Steps in Developing Threat Intelligence:

Collecting Data from Trusted Sources (A)

Gather data from threat intelligence feeds (e.g., STIX, TAXII, OpenCTI, VirusTotal, AbuseIPDB).

Include internal logs, honeypots, and third-party security vendors.

Analyzing and Correlating Threat Data (C)

Use correlation searches to match known threat indicators against live data.

Identify patterns in network traffic, logs, and endpoint activity.

Operationalizing Intelligence Through Workflows (E)

Automate responses using Splunk SOAR (Security Orchestration, Automation, and Response).

Enhance alert prioritization by integrating intelligence into risk-based alerting (RBA).

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