Splunk SPLK-5002 Practice Test - Questions Answers, Page 4

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.

Incorrect Answers: B. Limiting the number of correlation searches -- Reducing correlation searches may lead to missed threats. C. Using summary indexing for raw events -- Summary indexing improves performance but does not enhance risk-based detection.

Splunk Risk-Based Alerting Guide

Threat Intelligence in Splunk ES

How to Improve Data Indexing Performance in Splunk?

Optimizing indexing performance is critical for ensuring faster search speeds, better storage efficiency, and reduced latency in a Splunk deployment.

Why is 'Configuring Index-Time Field Extractions' Important? (Answer B)

Extracting fields at index time reduces the need for search-time processing, making searches faster.

Example: If security logs contain IP addresses, usernames, or error codes, configuring index-time extraction ensures that these fields are already available during searches.

Why 'Increasing the Number of Indexers in a Distributed Environment' Helps? (Answer D)

Adding more indexers distributes the data load, improving overall indexing speed and search performance.

Example: In a large SOC environment, more indexers allow for faster log ingestion from multiple sources (firewalls, IDS, cloud services).

Why Not the Other Options?

A. Indexing data with detailed metadata -- Adding too much metadata increases indexing overhead and slows down performance. C. Using lightweight forwarders for data ingestion -- Lightweight forwarders only forward raw data and don't enhance indexing performance.

Reference & Learning Resources

Splunk Indexing Performance Guide: https://docs.splunk.com/Documentation/Splunk/latest/Indexer/Howindexingworks Best Practices for Splunk Indexing Optimization: https://splunkbase.splunk.com Distributed Splunk Architecture for Large-Scale Environments: https://www.splunk.com/en_us/blog/tips-and-tricks

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.

Reference & 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

Why is Event Timestamping Important in Splunk?

Event timestamps help maintain the correct sequence of logs, ensuring that data is accurately analyzed and correlated over time.

Why 'Ensuring Events Are Organized Chronologically' is the Best Answer? (Answer D) Prevents event misalignment -- Ensures logs appear in the correct order. Enables accurate correlation searches -- Helps SOC analysts trace attack timelines. Improves incident investigation accuracy -- Ensures that event sequences are correctly reconstructed.

Example in Splunk: Scenario: A security analyst investigates a brute-force attack across multiple logs. Without correct timestamps, login failures might appear out of order, making analysis difficult. With proper event timestamping, logs line up correctly, allowing SOC analysts to detect the exact attack timeline.

Why Not the Other Options?

A. Assigning data to a specific sourcetype -- Sourcetypes classify logs but don't affect timestamps. B. Tagging events for correlation searches -- Correlation uses timestamps but timestamping itself isn't about tagging. C. Synchronizing event data with system time -- System time matters, but event timestamping is about chronological ordering.

Reference & Learning Resources

Splunk Event Timestamping Guide: https://docs.splunk.com/Documentation/Splunk/latest/Data/HowSplunkextractstimestamps Best Practices for Log Time Management in Splunk: https://www.splunk.com/en_us/blog/tips-and-tricks SOC Investigations & Log Timestamping: https://splunkbase.splunk.com

Understanding Risk-Based Prioritization

Risk-based prioritization is a methodology that evaluates both the likelihood and impact of risks to determine which threats require immediate action.

Why Risk-Based Prioritization?

Focuses on high-impact and high-likelihood risks first.

Helps SOC teams manage alerts effectively and avoid alert fatigue.

Used in SIEM solutions (Splunk ES) and Risk-Based Alerting (RBA).

Example in Splunk Enterprise Security (ES):

A failed login attempt from an internal employee might be low risk (low impact, low likelihood).

Multiple failed logins from a foreign country with a known bad reputation could be high risk (high impact, high likelihood).

Incorrect Answers:

A . Threat modeling Identifies potential threats but doesn't prioritize risks dynamically.

C . Incident lifecycle management Focuses on handling security incidents, not risk evaluation.

D . Statistical anomaly detection Detects unusual activity but doesn't prioritize based on impact.

Additional Resources:

Splunk Risk-Based Alerting (RBA) Guide

NIST Risk Assessment Framework

Splunk's REST API allows external applications and security tools to automate workflows, integrate with Splunk, and retrieve/search data programmatically.

Why Use REST APIs in Splunk Automation?

Automates interactions between Splunk and other security tools.

Enables real-time data ingestion, enrichment, and response actions.

Used in Splunk SOAR playbooks for automated threat response.

Example:

A security event detected in Splunk ES triggers a Splunk SOAR playbook via REST API to:

Retrieve threat intelligence from VirusTotal.

Block the malicious IP in Palo Alto firewall.

Create an incident ticket in ServiceNow.

Incorrect Answers:

A . To configure storage retention policies Storage is managed via Splunk indexing, not REST APIs.

C . To compress data before indexing Splunk does not use REST APIs for data compression.

D . To generate predefined reports Reports are generated using Splunk's search and reporting functionality, not APIs.

Additional Resources:

Splunk REST API Documentation

Automating Workflows with Splunk API

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

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 for managing detections. D. Summary indexing -- Stores precomputed search results but does not control detection content.

Reference & 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 Use 'Data Models' for Standardized Search Accuracy and Detection Logic?

Splunk Data Models provide a structured, normalized representation of raw logs, improving:

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

Example in Splunk Enterprise Security: Scenario: A SOC team monitors login failures across multiple authentication systems. Without Data Models: Different logs use src_ip, source_ip, or ip_address, making searches complex. With Data Models: All fields map to a standard format, enabling consistent detection logic.

Why Not the Other Options?

A. Field Extraction -- Extracts fields from raw events but does not standardize field names across sources. C. Event Correlation -- Detects relationships between logs but doesn't normalize data for search accuracy. D. Normalization Rules -- A general term; Splunk uses CIM & Data Models for normalization.

Reference & 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-