SPLK-5002 Splunk Certified Cybersecurity Defense Engineer Question and Answers

Why Use Risk-Based Dashboards for Tracking Threat Trends?

Risk-based dashboards in Splunk Enterprise Security (ES) provide a structured way to track threats over time.

🔹How Risk-Based Dashboards Help:✅Aggregate security events into risk scores → Helps prioritize high-risk activities.✅Show historical trends of threat activity.✅Correlate multiple risk factors across different security events.

💡Example in Splunk ES:🚀Scenario: A SOC team tracks insider threat activity over 6 months.✅The Risk-Based Dashboard shows:

Users with rising risk scores over time.

Patterns of malicious behavior (e.g., repeated failed logins + data exfiltration).

Correlation between different security alerts (e.g., phishing clicks → malware execution).

Why Not the Other Options?

❌A. Event sampling – Helps with performance optimization, not threat trend tracking.❌C. Summary indexing – Stores precomputed data but is not designed for tracking risk trends.❌D. Data model acceleration – Improves search speed, but doesn’t track security trends.

References & Learning Resources

📌Splunk ES Risk-Based Alerting Guide: https://docs.splunk.com/Documentation/ES 📌Tracking Security Trends Using Risk-Based Dashboards: https://splunkbase.splunk.com 📌How to Build Risk-Based Analytics in Splunk: https://www.splunk.com/en_us/blog/security

Automating case management workflows in Splunk streamlines incident response and reduces manual overhead, allowing analysts to focus on higher-value tasks.

Main Benefits of Automating Case Management:

Reduces Response Times (C)

Automatically assigns cases to analysts based on predefined rules.

Triggers playbooks and workflows in Splunk SOAR to handle common incidents.

Improves Analyst Productivity (C)

Reduces time spent on manual case creation and updates.

Provides integrated case tracking across Splunk and ITSM tools (e.g., ServiceNow, Jira).

Why Use "Content Management in Enterprise Security" for Detection Lifecycle Management?

The detection lifecycle refers to the process of creating, managing, tuning, and deprecating security detections over time. In Splunk Enterprise Security (ES), Content Management helps security teams:

✅Create, update, and retire correlation searches and security content✅Manage use case coverage for different threat categories✅Tune detection rules to reduce false positives✅Track changes in detection rules for better governance

💡Example in Splunk ES:🚀Scenario: A company updates its threat detection strategy based on new attack techniques.✅SOC analysts use Content Management in ES to:

Review existing correlation searches

Modify detection logic to adapt to new attack patterns

Archive outdated detections and enable new MITRE ATT&CK techniques

Why Not the Other Options?

❌A. Data model acceleration – Improves search performance but does not manage detection lifecycles.❌C. Metrics indexing – Used for time-series data (e.g., system performance monitoring), not formanaging detections.❌D. Summary indexing – Stores precomputed search results but does not control detection content.

References & Learning Resources

📌Splunk ES Content Management Documentation: https://docs.splunk.com/Documentation/ES 📌Best Practices for Security Content Management in Splunk ES: https://www.splunk.com/en_us/blog/security 📌MITRE ATT&CK Integration with Splunk: https://attack.mitre.org/resources

Why is Asset and Identity Information Important in Correlation Searches?

Correlation searches in Splunk Enterprise Security (ES) analyze security events to detect anomalies, threats, and suspicious behaviors. Adding asset and identity information significantly improves security detection and response by:

1️⃣Enhancing the Context of Detections – (Answer A)

Helps analysts understand the impact of an event by associating security alerts with specific assets and users.

Example: If a failed login attempt happens on a critical server, it’s more serious than one on a guest user account.

2️⃣Prioritizing Incidents Based on Asset Value – (Answer C)

High-value assets (CEO’s laptop, production databases) need higher priority investigations.

Example: If malware is detected on a critical finance server, the SOC team prioritizes it over a low-impact system.

Why Not the Other Options?

❌B. Reducing the volume of raw data indexed – Asset and identity enrichment adds more metadata;it doesn’t reduce indexed data.❌D. Accelerating data ingestion rates – Adding asset identity doesn’t speed up ingestion; it actually introduces more processing.

References & Learning Resources

📌Splunk ES Asset & Identity Framework: https://docs.splunk.com/Documentation/ES/latest/Admin/Assetsandidentitymanagement 📌Correlation Searches in Splunk ES: https://docs.splunk.com/Documentation/ES/latest/Admin/Correlationsearches

Aggregation policies in Splunk Enterprise Security (ES) are used to group related notable events, reducing alert fatigue and improving incident analysis.

Role of Aggregation Policies in Correlation Searches:

Group Related Notable Events (A)

Helps SOC analysts see a single consolidated event instead of multiple isolated alerts.

Uses common attributes like user, asset, or attack type to aggregate events.

Improves Incident Response Efficiency

Reduces the number of duplicate alerts, helping analysts focus on high-priority threats.

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.

References & 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

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

The GET method in the Splunk REST API is used to retrieve data from a Splunk index. It allows users and automated scripts to fetch logs, alerts, or query results programmatically.

Key Points About GET in Splunk API:

Used for searching and retrieving logs from indexes.

Can be used to get search results, job status, and Splunk configuration details.

Common API endpoints include:

/services/search/jobs/{search_id}/results– Retrieves results of a completed search.

/services/search/jobs/export– Exports search results in real-time.

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:

1️⃣Obtain API Credentials – Get API keys or authentication tokens from the vulnerability management tool.2️⃣Configure REST API Integration – Use Splunk SOAR’s built-in API connectors or create a custom REST API call.3️⃣Ingest Vulnerability Data into Splunk – Map API responses to Splunk ES correlation searches.4️⃣Automate 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.

References & 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

What Are Risk-Based Detections in Splunk?

Risk-based detections in Splunk Enterprise Security (ES) assign risk scores to security events based on threat severity and asset criticality.

🔹Key Components of Risk-Based Detections:1️⃣Risk Modifiers – Adjusts risk scores based on event type (e.g., failed logins, malware detections).2️⃣Risk Objects – Entities associated with security events (e.g., users, IPs, devices).3️⃣Risk Scores – Numerical values indicating the severity of a risk.

💡Example in Splunk Enterprise Security:🚀Scenario: A high-privilege account (Admin) fails multiple logins from an unusual location.✅Splunk ES applies risk-based detection:

Failed logins add +10 risk points

Login from a suspicious country adds +15 points

Total risk score exceeds 25 → Triggers an alert

Why Not the Other Options?

❌B. Summary indexing, tags, and event types – Summary indexing stores precomputed data, but doesn’t drive risk-based detection.❌C. Alerts, notifications, and priority levels – Important, but risk-based detection is based on scoring, not just alerts.❌D. Source types, correlation searches, and asset groups – Helps in data organization, but not specific to risk-based detections.

References & Learning Resources

📌Splunk ES Risk-Based Alerting Guide: https://docs.splunk.com/Documentation/ES 📌Risk-Based Detections & Scoring in Splunk: https://www.splunk.com/en_us/blog/security/risk-based-alerting.html 📌Best Practices for Risk Scoring in SOC Operations: https://splunkbase.splunk.com

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.

Splunk SOAR (Security Orchestration, Automation, and Response) playbooks automate security processes, reducing response times.

✅1. Defined Workflows (A)

A structured flowchart of actions for handling security events.

Ensures that the playbook follows a logical sequence (e.g., detect → enrich → contain → remediate).

Example:

If a phishing email is detected, the workflow includes:

Extract email artifacts (e.g., sender, links).

Check indicators against threat intelligence feeds.

Quarantine the email if it is malicious.

✅2. Actionable Steps or Tasks (C)

Each playbook contains specific, automated steps that execute responses.

Examples:

Extracting indicators from logs.

Blocking malicious IPs in firewalls.

Isolating compromised endpoints.

✅3. Integration with External Tools (E)

Playbooks must connect with SIEM, EDR, firewalls, threat intelligence platforms, and ticketing systems.

Uses APIs and connectors to integrate with tools like:

Splunk ES

Palo Alto Networks

Microsoft Defender

ServiceNow

❌Incorrect Answers:

B. Threat intelligence feeds → These enrich playbooks but are not mandatory components of playbook development.

D. Manual approval processes → Playbooks are designed for automation, not manual approvals.

📌Additional Resources:

Splunk SOAR Playbook Documentation

Best Practices for Developing SOAR Playbooks

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

A SOAR (Security Orchestration, Automation, and Response) playbook is a set of automated actions designed to respond to security incidents. Before deploying it in a live environment, a security analyst must ensure that it operates correctly, minimizes false positives, and doesn’t disrupt business operations.

🛠Key Reasons for Using Simulated Incidents:

Ensures that the playbook executes correctly and follows the expected workflow.

Identifies false positives or incorrect actions before deployment.

Tests integrations with other security tools (SIEM, firewalls, endpoint security).

Provides a controlled testing environment without affecting production.

How to Test a Playbook in Splunk SOAR?

1️⃣Use the "Test Connectivity" Feature – Ensures that APIs and integrations work.2️⃣Simulate an Incident – Manually trigger an alert similar to a real attack (e.g., phishing email or failed admin login).3️⃣Review the Execution Path – Check each step in the playbook debugger to verify correct actions.4️⃣Analyze Logs & Alerts – Validate that Splunk ES logs, security alerts, and remediation steps are correct.5️⃣Fine-tune Based on Results – Modify the playbook logic to reduce unnecessary alerts or excessive automation.

Why Not the Other Options?

❌B. Monitor the playbook’s actions in real-time environments – Risky without prior validation. Itcan cause disruptions if the playbook misfires.❌C. Automate all tasks immediately – Not best practice. Gradual deployment ensures better security control and monitoring.❌D. Compare with existing workflows – Good practice, but it does not validate the playbook’s real execution.

References & Learning Resources

📌Splunk SOAR Documentation: https://docs.splunk.com/Documentation/SOAR 📌Testing Playbooks in Splunk SOAR: https://www.splunk.com/en_us/products/soar.html 📌SOAR Playbook Debugging Best Practices: https://splunkbase.splunk.com

Why Use "Data Models" for Standardized Search Accuracy and Detection Logic?

SplunkData Modelsprovide astructured, normalized representationof raw logs, improving:

✅Search consistency across different log sources✅Detection logic by ensuring standardized field names✅Faster and more efficient querieswith data model acceleration

💡Example in Splunk Enterprise Security:🚀Scenario:A SOC team monitors login failures acrossmultiple authentication systems.✅Without Data Models:Different logs usesrc_ip, source_ip, or ip_address, making searches complex.✅With Data Models:All fieldsmap to a standard format, enablingconsistent detection logic.

Why Not the Other Options?

❌A. Field Extraction– Extracts fields from raw events butdoes not standardize field names across sources.❌C. Event Correlation– Detects relationships between logsbut doesn’t normalize data for search accuracy.❌D. Normalization Rules– A general term; Splunkuses CIM & Data Models for normalization.

References & Learning Resources

📌Splunk Data Models Documentation: https://docs.splunk.com/Documentation/Splunk/latest/Knowledge/Aboutdatamodels 📌Using CIM & Data Models for Security Analytics: https://splunkbase.splunk.com/app/263 📌How Data Models Improve Search Performance: https://www.splunk.com/en_us/blog/tips-and-

Splunk SOAR (Security Orchestration, Automation, and Response) playbooks help SOC teams automate, orchestrate, and respond to threats faster.

✅Key Benefits of SOAR Playbooks

Automates Repetitive Tasks

Reduces manual workload for SOC analysts.

Automates tasks like enriching alerts, blocking IPs, and generating reports.

Orchestrates Multiple Security Tools

Integrates with firewalls, EDR, SIEMs, threat intelligence feeds.

Example: A playbook can automatically enrich an IP address by querying VirusTotal, Splunk, and SIEM logs.

Accelerates Incident Response

Reduces Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR).

Example: A playbook can automatically quarantine compromised endpoints in CrowdStrike after an alert.

❌Incorrect Answers:

A. Manually running searches across multiple indexes → SOAR playbooks are about automation, not manual searches.

C. Improving dashboard visualization capabilities → Dashboards are part of SIEM (Splunk ES), not SOAR playbooks.

D. Enhancing data retention policies → Retention is a Splunk Indexing feature, not SOAR-related.

📌Additional Resources:

Splunk SOAR Playbook Guide

Automating Threat Response with SOAR

In Splunk Enterprise Security (ES), notable events are generated by correlation searches, which are predefined searches designed to detect security incidents by analyzing logs and alerts from multiple data sources. Adding additional context to these notable events enhances their value for analysts and improves the efficiency of incident response.

To incorporate additional context, you can:

Use lookup tables to enrich data with information such as asset details, threat intelligence, and user identity.

Leverage KV Store or external enrichment sources like CMDB (Configuration Management Database) and identity management solutions.

Apply Splunk macros orevalcommands to transform and enhance event data dynamically.

Use Adaptive Response Actions in Splunk ES to pull additional information into a notable event.

The correct answer is A. By adding enriched fields during search execution, because enrichment occurs dynamically during search execution, ensuring that additional fields (such as geolocation, asset owner, and risk score) are included in the notable event.

References:

Splunk ES Documentation on Notable Event Enrichment

Correlation Search Best Practices

Using Lookups for Data Enrichment

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).

How to Improve Dashboard Accuracy in Splunk?

🔹1. Using Accelerated Data Models (Answer A)✅Increases search speedand ensuresdashboards load faster.✅Provides pre-processed structured dataforreal-time analysis.✅Example:ASOC dashboard tracking failed loginsuses an accelerated authentication data model forfaster rendering.

🔹2. Performing Regular Data Validation (Answer C)✅Ensures that the indexed data is accurate and complete.✅Prevents misleading dashboardscaused by incomplete logs or incorrect field extractions.✅Example:If afirewall log source stops sending data, regular validation detects missing logsbefore analysts rely on incorrect dashboards.

Why Not the Other Options?

❌B. Avoiding token-based filters– Tokensimprovedashboard flexibility; avoiding themreduces usability.❌D. Disabling drill-down features– Drill-downsenhance insightsby allowing analysts to investigate details easily.

References & Learning Resources

📌Splunk Dashboard Performance Optimization: https://docs.splunk.com/Documentation/Splunk/latest/Viz/Dashboards 📌Using Data Models for Fast and Accurate Dashboards: https://splunkbase.splunk.com 📌Regular Data Validation for SOC Dashboards: https://www.splunk.com/en_us/blog/security

Why Maintain a Detection Lifecycle?

Adetection lifecycleensures that security alerts, correlation searches, and automation playbooks arecontinuously refinedto maintainaccuracy, efficiency, and relevanceagainst modern threats.

🔹1. Detecting and Eliminating Outdated Searches (Answer A)✅Removes unnecessary or redundant correlation searchesthat may slow down performance.✅Prevents false positivescaused by outdated detection logic.✅Example:A Splunk ES search for anold malware variantmay no longer be effective → it should be updated to detectnew techniques used by attackers.

🔹2. Ensuring Detections Remain Relevant to Evolving Threats (Answer C)✅Regular updatesensure thatnew MITRE ATT&CK techniquesand threat indicators are included.✅Example:If attackers start usingLiving-off-the-Land (LotL) techniques, security teams mustupdate detection rules to identify suspicious PowerShell activity.

Why Not the Other Options?

❌B. Scaling the Splunk deployment effectively– Lifecycle management improvesdetection accuracy, notinfrastructure scalability.❌D. Automating the deployment of new detection logic– Automation helps, but lifecycle management isabout reviewing and updating detections, not just deployment.

References & Learning Resources

📌Detection Management in Splunk ES: https://docs.splunk.com/Documentation/ES 📌Updating Threat Detections Using MITRE ATT&CK in Splunk: https://attack.mitre.org/resources 📌Best Practices for SOC Detection Engineering: https://splunkbase.splunk.com

Updating the SOP for Handling Phishing Incidents

AStandard Operating Procedure (SOP)should focus onprevention, detection, and response.

✅1. Documenting Steps for User Awareness Training (C)

Training employeeshelps prevent phishing incidents.

Example:

Teach users toidentify phishing emails and report them via a Splunk SOAR playbook.

❌Incorrect Answers:

A. Ensuring all reports are manually verified by analysts→Automation(via SOAR) should be used forinitial triage.

B. Automating the isolation of suspected phishing emails→ Automation is useful, butuser education prevents incidents.

D. Reporting incidents to the executive board immediately→Only major security breachesshould beescalated to executives.

📌Additional Resources:

NIST Incident Response Guide

Splunk Phishing Detection Playbooks