Free Practice Questions for Splunk SPLK-5001 Exam

Splunk Enterprise Security (ES) provides various features to enhance security monitoring, analysis, and incident response. One of the powerful features in Splunk ES isAnnotations. This feature allows security analysts to map and categorize correlation search results according to well-known industry frameworks such as the CIS Critical Security Controls, MITRE ATT&CK, and the Lockheed Martin Cyber Kill Chain®.

Purpose of Annotations:

Annotations help analysts understand and categorize security events by aligning them with recognized security frameworks. This alignment provides context, making it easier to understand the nature of threats and how they fit within broader threat models or attack strategies.

How Annotations Work:

When a correlation search in Splunk ES triggers an alert, Annotations can automatically tag the alert with relevant tactics, techniques, and procedures (TTPs) from frameworks like MITRE ATT&CK. This helps in categorizing the event within the context of known attack patterns, offering insights into potential next steps by an attacker and recommended defensive actions.

Annotations can be manually added or configured to be applied automatically based on the nature of the search results.

Integration with Frameworks:

MITRE ATT&CK:Annotations can map alerts to specific techniques and tactics in the MITRE ATT&CK framework, which provides a detailed knowledge base of adversary behaviors, tactics, and techniques.

CIS Critical Security Controls:These controls can also be mapped through annotations, allowing the organization to measure and improve its security posture against these controls.

Lockheed Martin Cyber Kill Chain®:This model focuses on the stages of a cyberattack, and annotations can help identify where in the kill chain a particular alert fits, providing a clearer understanding of the attack’s progression.

Annotations in Splunk ES:Practical Example:Consider a correlation search that detects unusual behavior indicating potential lateral movement within a network. If this alert is annotated with a reference to the MITRE ATT&CK framework, it might map to techniques like "T1021 - Remote Services," which is associated with the lateral movement tactic. This mapping not only categorizes the event but also helps in planning the next steps for containment and investigation.

Efficiency in Response:By aligning alerts with industry frameworks, annotations help in quickly identifying the nature and potential impact of a threat.

Consistency in Analysis:Provides a standardized method for categorizing and responding to alerts, ensuring that all analysts interpret and react to threats in a consistent manner.

Improved Reporting:Allows for better visualization and reporting of threats according to established frameworks, making it easier to communicate risks and actions to stakeholders.

The scenario described is an example ofLeast Frequency of Occurrence Analysis. This threat-hunting technique focuses on identifying events or behaviors that occur infrequently, under the assumption that rare activities could indicate abnormal or suspicious behavior. By filtering out users who log in frequently and focusing on those with rare login attempts, the threat hunter aims to identify potentially suspicious activity that warrants further investigation. This technique is particularly effective in detecting stealthy attacks that might evade more common detection methods.

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TheAccess dashboardsin Splunk Enterprise Security focus on authentication, login activity, access controls, and related security events. These dashboards provide analysts with insights into successful and failed authentications, anomalous access patterns, and identity-related events.

Audit dashboardsgenerally focus on compliance and audit logs.

Asset and Identity dashboardsprovide broader context about users and devices but are not specifically focused on authentication events.

Endpoint dashboardsconcentrate on host and endpoint telemetry such as process execution and file activity.

Splunk’sEnterprise Security User Guideclearly identifies Access dashboards as the primary interface for monitoring and investigating authentication and access behaviors.

TheCHMC (Cybersecurity Health Management Capability)framework was created specifically to assess and measure an organization's cybersecurity maturity. Unlike compliance frameworks that focus on specific regulatory requirements (e.g., PCI-DSS for payment card security, GDPR for data privacy, or FISMA for federal information security management), CHMC provides a structured maturity model that evaluates the effectiveness and sophistication of cybersecurity practices within an enterprise.

TheCHMCframework benchmarks capabilities across various domains such as governance, risk management, incident response, and security operations, enabling organizations to identify gaps and prioritize improvements systematically.

PCI-DSSis a compliance standard aimed at securing payment card data.

GDPRgoverns data protection and privacy for individuals in the EU.

FISMAmandates federal agencies to implement information security programs but does not itself provide a maturity model.

TheSplunk Cybersecurity Defense Analyst materialshighlight that maturity models like CHMC help organizations progress beyond basic compliance, fostering a culture of continuous improvement in cybersecurity posture.

In Splunk Enterprise Security, theRisk Event Timelineprovides a chronological view of risk events associated with a particular Risk Object, such as a user or device. This timeline helps analysts visualize and understand the sequence and nature of risk events over time, aiding in the investigation of security incidents.

Risk Event Timeline:

The Risk Event Timeline is accessible by clicking the risk event count associated with a Risk Object in the Incident Review dashboard. This action opens up the timeline view, which provides a detailed chronological perspective on how risk events have unfolded.

This feature is particularly useful for tracking the progression of threats and understanding the context of incidents.

Incorrect Options:

A. Running the Risk Analysis Adaptive Response action within the Notable Event:This option pertains to running a response action rather than visualizing risk events over time.

B. Via a workflow action for the Risk Investigation dashboard:Although workflow actions can lead to various dashboards, the specific visualization described is accessed via the Risk Event Timeline.

C. Via the Risk Analysis dashboard under the Security Intelligence tab in Enterprise Security:While this dashboard provides valuable insights into risk data, the specific chronological visualization is found in the Risk Event Timeline.

In the context of Intrusion Detection Systems (IDS), determining whether an event is a True Negative, True Positive, False Negative, or False Positive depends on the system's detection and the reality of the situation.

Let's break down the scenario:

IDS Signature Explanation:

The IDS is set to detect and alert on logins to a server, but only if they happen during a specific time window, from 6:00 PM to 6:00 AM.

The question states that no alerts occur during this time frame, but the IDS signature is known to be correct.

Understanding Detection Terms:

True Positive: The IDS correctly detects an intrusion or suspicious activity that is actually happening.

True Negative: The IDS does not detect any activity because no suspicious or malicious activity is occurring, and this lack of detection is correct.

False Positive: The IDS detects an intrusion or activity, but it is a false alarm (i.e., there is no real threat).

False Negative: The IDS fails to detect a real intrusion or activity when it should have, missing a legitimate alert.

Applying the Scenario:

In this case, no IDS alerts occurred during the specified time frame. If there were no actual logins during this period and the signature was designed correctly, then the absence of alerts is expected and appropriate.

Since no suspicious logins occurred, and the IDS did not trigger any alerts, this situation represents a True Negative—the system correctly identified that there was no suspicious activity to alert on.

Why the Answer is "True Negative":

The IDS signature is working as expected.

The condition that would trigger an alert (logins during the specified time) did not happen, so the lack of alerts is a correct response.

Therefore, this is classified as a True Negative because no malicious activity took place, and the IDS correctly refrained from raising an alert.

Comparison to Other Options:

B. True Positive – This would indicate that an alert occurred because of actual suspicious activity, but in this case, no alerts occurred.

C. False Negative – This would mean that suspicious activity occurred, but the IDS failed to detect it. In this case, there was no activity to detect, so this option is not correct.

D. False Positive – This would suggest the IDS raised an alert when no suspicious activity happened, but again, no alerts occurred, so this doesn’t apply.

Mean Time to Respond (MTTR)measures the average time it takes for an analyst to investigate and disposition an incident after detection. Improvements in dashboard customization that provide quicker access to relevant data, better visualizations, or faster workflows can reduce MTTR by enhancing analyst efficiency.

Mean Time to Detectmeasures how quickly an incident is identified, not analyst efficiency after detection.

Recovery Timerefers to the time taken to restore systems to normal after an incident.

Dwell Timeis the time an adversary remains undetected within a network.

TheSplunk Cybersecurity Defense Analyst materialshighlight MTTR as a key SOC performance metric influenced by operational improvements, including better dashboards and automated workflows.

Splunk SOAR (Security Orchestration, Automation, and Response) Playbooks are designed to automate repetitive and orchestratedresponse actions, such as containment or remediation on compromised systems. The use case oftaking containment action on a compromised hostfits perfectly, as playbooks execute sequences like isolating the host, killing processes, or blocking network communications automatically or with analyst approval.

Forming hypothesis for threat huntingis a manual, analytical process not suitable for automation via playbooks.

Creating persistent field extractionsis a data parsing task performed in Splunk searches or configurations, unrelated to SOAR playbooks.

Visualizing complex datasetsis a reporting and analytics task done with dashboards or apps, not SOAR workflows.

TheSplunk SOAR documentationexplicitly defines playbooks as automation workflows to accelerate incident response and reduce analyst workload on containment and investigation.

The fastest SPL (Search Processing Language) search in Splunk is one that leverages index and sourcetype constraints upfront to minimize the amount of data scanned, thereby reducing search latency significantly. According to theSplunk Cybersecurity Defense Analyst Study Guide, searches that specifyindexandsourcetypein the base search filter data more efficiently by restricting the search scope immediately at the indexing layer before any processing occurs. This contrasts with searches that filter fields only after the raw data is retrieved, which is less efficient.

Option D explicitly specifies theindex-networkandsourcetype=netflow, combined with the exact field filterssrc_ip=1.2.3.4,src_port=2938, andprotocol=top. This precise filtering directly reduces the dataset Splunk has to scan.

Options A and C use theindex-networkbut filter some fields after the initial search (e.g., using search src_ip=1.2.3.4 after stats aggregation), causing unnecessary processing overhead.

Option B lacks any index or sourcetype specification, meaning Splunk performs a much broader search across all indexes and data, which is slower.

TheSplunk Cybersecurity Defense Analyst official documentationemphasizes that applying early filtering with index and sourcetype limits is crucial for search performance, particularly in high-volume network data scenarios.