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NEW QUESTION # 68
Which of the following is a reason to utilize ES risk framework as a part of detection building?
Answer: A
Explanation:
The Enterprise Security Risk Framework enables detection engineers to express suspicious observations as risk against meaningful entities-typically users, systems, or other risk objects-and then prioritize those observations according to their security significance. Option C therefore represents the principal value being tested: prioritizing findings based on potential business impact .
In a Risk-Based Alerting design, an individual behavior does not necessarily need to generate an analyst- facing finding immediately. Instead, detections can generate risk events containing fields such as the risk object, risk object type, risk score, and contextual annotations. Multiple risk events can accumulate until correlation logic determines that the combined evidence warrants escalation. Risk Factors and asset/identity context can further modify significance when an affected entity is particularly sensitive or critical.
Risk processing is therefore fundamentally about contextual prioritization and evidence aggregation , not search-performance acceleration. It does not inherently create a threat-intelligence feedback loop, nor is its primary purpose to simplify SOAR execution. Those capabilities can interact with risk-based detections but are separate functions.
Study Guide topics: Enterprise Security Risk Framework; Risk-Based Alerting; risk objects; risk scores; business impact; security finding prioritization.
NEW QUESTION # 69
A Splunk administrator needs to integrate a third-party vulnerability management tool to automate remediation workflows. What is the most efficient first step?
Answer: A
Explanation:
Using the third-party product ' s REST API with Splunk SOAR is the most efficient approach because SOAR is designed to orchestrate external security technologies programmatically. A REST integration allows SOAR to retrieve vulnerability information, enrich assets, initiate remediation actions, track execution status, and incorporate those operations into repeatable playbooks.
A typical workflow could use API requests to obtain vulnerability findings, identify the affected asset, determine severity, invoke a remediation or ticketing action, and subsequently verify completion. The uploaded course material emphasizes REST communication, SOAR assets, HTTP methods, authentication, and automated playbook actions as core integration concepts.
Manual alerts do not accomplish automation. Creating a separate correlation search for every vulnerability type creates unnecessary detection-management overhead and does not provide the remediation integration itself. Dashboards improve visibility but remain observational rather than actionable.
The REST approach also separates concerns appropriately: the vulnerability platform remains the source of vulnerability-management capability, while SOAR coordinates the workflow through authenticated API calls and automation logic.
Study Guide topics: Splunk SOAR, REST APIs, third-party integrations, playbooks, automated remediation, API authentication, orchestration.
NEW QUESTION # 70
Which features of Splunk are crucial for tuning correlation searches?(Choosethree)
Answer: C,D,E
Explanation:
Correlation searches are a key component of Splunk Enterprise Security (ES) that help detect and alert on security threats by analyzing machine data across various sources. Proper tuning of these searches is essential to reduce false positives, improve performance, and enhance the accuracy of security detections in a Security Operations Center (SOC).
Crucial Features for Tuning Correlation Searches
#1. Using Thresholds and Conditions (A)
Thresholds help control the sensitivity of correlation searches by defining when a condition is met.
Setting appropriate conditions ensures that only relevant events trigger notable events or alerts, reducing noise.
Example:
Instead of alerting on any failed login attempt, a threshold of 5 failed logins within 10 minutes can be set to identify actual brute-force attempts.
#2. Reviewing Notable Event Outcomes (B)
Notable events are generated by correlation searches, and reviewing them is critical for fine-tuning.
Analysts in the SOC should frequently review false positives, duplicates, and low-priority alerts to refine rules.
Example:
If a correlation search is generating excessive alerts for normal user activity, analysts can modify it to exclude known safe behaviors.
#3. Optimizing Search Queries (E)
Efficient Splunk Search Processing Language (SPL) queries are crucial to improving search performance.
Best practices include:
Using index-time fields instead of extracting fields at search time.
Avoiding wildcards and unnecessary joins in searches.
Using tstats instead of regular searches to improve efficiency.
Example:
Using:
| tstats count where index=firewall by src_ip
instead of:
index=firewall | stats count by src_ip
can significantly improve performance.
Incorrect Answers & Explanation
#C. Enabling Event Sampling
Event sampling helps analyze a subset of events to improve testing but does not directly impact correlation search tuning in production.
In a SOC environment, tuning needs to be based on actual real-time event volumes, not just sampled data.
#D. Disabling Field Extractions
Field extractions are essential for correlation searches because they help identify and analyze security-related fields (e.g.,user,src_ip,dest_ip).
Disabling them would limit the visibility of important security event attributes, making detections less effective.
Additional Resources for Learning
#Splunk Documentation & Learning Paths:
Splunk ES Correlation Search Documentation
Best Practices for Writing SPL
Splunk Security Essentials - Use Cases
SOC Analysts Guide for Correlation Search Tuning
#Courses & Certifications:
Splunk Enterprise Security Certified Admin
Splunk Core Certified Power User
Splunk SOAR Certified Automation Specialist
NEW QUESTION # 71
When creating a detection that searches user activity across CIM-compliant data, which CIM field should be reviewed to ensure that data is aggregated appropriately?
Answer: D
Explanation:
The user field is the normalized CIM field for user activity across data sources. Reviewing and using this field ensures that data from different sources is properly aggregated, enabling consistent detection logic across CIM-compliant datasets.
NEW QUESTION # 72
An engineer has been asked to build a new dashboard after an increase in login failures across the organization's Microsoft Azure domain. They need to construct a search to only display failed logins for their Azure Active Directory users, and choose a visualization that will help analysts quickly identify failed logins that originate outside of North America. Which of the following search and visualization type combinations will achieve this?
Answer: D
Explanation:
The correct sourcetype for Azure Active Directory sign-ins is ms:aad:signin, and filtering on loginStatus=Failure ensures only failed logins are shown. Using geostats with latitude and longitude fields allows plotting login attempts geographically, and a Cluster Map visualization is best for quickly identifying failed logins originating outside of North America.
NEW QUESTION # 73
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