Splunk SPLK-5002 - Splunk Certified Cybersecurity Defense Engineer

How to Improve Splunk’s Automation Efficiency?

Splunk's automation capabilities rely on efficient data ingestion, optimized searches, and automated response workflows. The following methods help improve Splunk’s automation:

🔹1. Using Modular Inputs (Answer A)

Modular inputs allow Splunk to ingest third-party data efficiently (e.g., APIs, cloud services, or security tools).

Benefit: Improves automation by enabling real-time data collection for security workflows.

Example: Using a modular input to ingest threat intelligence feeds and trigger automatic responses.

🔹2. Optimizing Correlation Search Queries (Answer B)

Well-optimized correlation searches reduce query time and false positives.

Benefit: Faster detections → Triggers automated actions in SOAR with minimal delay.

Example: Usingtstatsinstead of raw searches for efficient event detection.

🔹3. Employing Prebuilt SOAR Playbooks (Answer E)

SOAR playbooks automate security responses based on predefined workflows.

Benefit: Reduces manual effort in phishing response, malware containment, etc.

Example: Automating phishing email analysis using a SOAR playbook that extracts attachments, checks URLs, and blocks malicious senders.

Why Not the Other Options?

❌C. Leveraging saved search acceleration – Helps with dashboard performance, but doesn’t directly improve automation.❌D. Implementing low-latency indexing – Reduces indexing lag but is not a core automation feature.

References & Learning Resources

📌Splunk SOAR Automation Guide: https://docs.splunk.com/Documentation/SOAR 📌Optimizing Correlation Searches in Splunk ES: https://docs.splunk.com/Documentation/ES 📌Prebuilt SOAR Playbooks for Security Automation: https://splunkbase.splunk.com

When a correlation search in Splunk Enterprise Security (ES) generates excessive notable events due to test accounts, the best approach is to filter out test accounts while keeping legitimate detections active.

✅1. Apply Filtering to Exclude Test Accounts (B)

Modifies the correlation search to exclude known test accounts.

Reduces false positives while keeping real threats visible.

Example:

Update the search to exclude test accounts:

index=auth_logs NOT user IN ("test_user1", "test_user2")

❌Incorrect Answers:

A. Disable the correlation search for test accounts → This removes visibility into all failed logins, including those that may indicate real threats.

C. Lower the search threshold for failed logins → Would increase false positives, making it harder for SOC teams to focus on real attacks.

D. Suppress all notable events temporarily → Suppression hides all alerts, potentially missing real security incidents.

📌Additional Resources:

Splunk ES: Managing Correlation Searches

Reducing False Positives in SIEM

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

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.

Why Use Real-Time Notable Event Dashboards for Phishing Detection?

Phishing campaigns require real-time monitoring to detect threats as they emerge and respond quickly.

🔹Why "Real-Time Notable Event Dashboards" is the Best Choice? (Answer B)✅Shows live security alerts for phishing detections.✅Enables SOC analysts to take immediate action (e.g., blocking malicious domains, disabling compromised accounts).✅Uses correlation searches in Splunk Enterprise Security (ES) to detect phishing indicators.

💡Example in Splunk:🚀Scenario: A company runs a phishing awareness campaign.✅Real-time dashboards track:

How many employees clicked on phishing links.

How many users reported phishing emails.

Any suspicious activity (e.g., account takeovers).

Why Not the Other Options?

❌A. Weekly incident trend reports – Helpful for analysis but not fast enough for phishing detection.❌C. Risk score-based summary reports – Risk scores are useful but not designed for real-time phishing detection.❌D. SLA compliance reports – SLA reports measure performance but don’t help actively detect phishing attacks.

References & Learning Resources

📌Splunk ES Notable Events & Phishing Detection: https://docs.splunk.com/Documentation/ES 📌Real-Time Security Monitoring with Splunk: https://splunkbase.splunk.com 📌SOC Dashboards for Phishing Campaigns: https://www.splunk.com/en_us/blog/tips-and-tricks

Summary indexing in Splunk improves search efficiency by storing pre-aggregated data, reducing the need to process large datasets repeatedly.

Key Benefits of Summary Indexing:

Improves Search Performance on Aggregated Data (B)

Reduces query execution time by storing pre-calculated results.

Helps SOC teams analyze trends without running resource-intensive searches.

Increases Data Retention Period (D)

Raw logs may have short retention periods, but summary indexes can store key insights for longer.

Useful for historical trend analysis and compliance reporting.

Splunk prevents duplicate data from being indexed through event parsing, which occurs during the data ingestion process.

How Event Parsing Prevents Duplicate Data:

Splunk’s indexer parses incoming data and assigns unique timestamps, metadata, and event IDs to prevent reindexing duplicate logs.

CRC Checks (Cyclic Redundancy Checks) are applied to avoid duplicate event ingestion.

Index-time filtering and transformation rules help detect and drop repeated data before indexing.

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.

Why Configure Asset & Identity Information for Privileged Accounts First?

Risk-based detection focuses on identifying and prioritizing threats based on the severity of their impact. For privileged accounts (admins, domain controllers, finance users), understanding who they are, what they access, and how they behave is critical.

🔑Key Steps for Risk-Based Detection in Splunk ES:1️⃣Define Privileged Accounts & Groups – Identify high-risk users (Admin, HR, Finance, CISO).2️⃣Assign Risk Scores – Apply higher scores to actions involving privileged users.3️⃣Enable Identity & Asset Correlation – Link users to assets for better detection.4️⃣Monitor for Anomalies – Detect abnormal login patterns, excessive file access, or unusual privilege escalation.

🔹Example in Splunk ES:

A domain admin logs in from an unusual location → Trigger high-risk alert

A finance director downloads sensitive payroll data at midnight → Escalate for investigation

Why Not the Other Options?

❌B. Correlation searches with low thresholds – May generate excessive false positives, overwhelming the SOC.❌C. Event sampling for raw data – Doesn’t provide context for risk-based detection.❌D. Automated dashboards for all accounts – Useful for visibility, but not the first step for risk-based security.

References & Learning Resources

📌Splunk ES Risk-Based Alerting (RBA): https://www.splunk.com/en_us/blog/security/risk-based-alerting.html 📌Privileged Account Monitoring in Splunk: https://docs.splunk.com/Documentation/ES/latest/User/RiskBasedAlerting 📌Implementing Privileged Access Security (PAM) with Splunk: https://splunkbase.splunk.com