SPLK-5001 Sample Questions Answers

The key requirement is to calculate arunning total of distinct IP addressesover time, which involves both aggregation (stats dc(clientip) by _time) and cumulative calculation (streamstats dc(ipa) as "Cumulative total").

Option Acorrectly bins events by day (bin _time span=1d), calculates distinct counts (dc(clientip)) per day, and then applies streamstats to compute the running (cumulative) count across the timeline.

Option Bcalculates daily distinct counts but does not provide a running total.

Option Csimply formats a table without aggregation.

Option Daggregates by user, not by day, and misses cumulative counts.

TheSplunk Search Manualrecommends using streamstats with distinct count fields to obtain running totals for time series data.

Adaptive Response is a feature in Splunk's Enterprise Security (ES) framework that allows security teams to automate actions and responses based on alerts or notable events. This feature is pivotal for orchestrating automated incident response processes, reducing the time between detection and response, and integrating Splunk with external systems to trigger appropriate actions.

Purpose:Adaptive Response enables the automation of specific tasks or workflows based on security events detected by Splunk ES. For instance, it can trigger actions such as isolating a compromised host, blocking IP addresses, or enriching data by querying additional sources when a notable event occurs.

Mechanism:When a notable event is identified within the Splunk platform, Adaptive Response can execute a series of predefined actions. These actions can be configured within the Splunk interface, allowing them to run automatically or with manual approval depending on the organization's needs. This capability is essential for streamlining security operations, especially in environments where quick response is critical.

Integration with External Applications:One of the key features of Adaptive Response is its ability to integrate with third-party security tools and solutions. This integration extends the capabilities of Splunk by allowing it to interact with other systems likefirewalls, intrusion prevention systems (IPS), endpoint detection and response (EDR) tools, and ticketing systems. This ensures a coordinated and comprehensive defense mechanism.

Usage in Security Operations:Security analysts often rely on Adaptive Response for managing and automating common security tasks, such as:

Quarantine or isolate a hostin response to malware detection.

Trigger a full disk scanwhen suspicious activity is detected.

Notify relevant stakeholdersthrough ticketing systems or direct communication tools.

Update firewall rulesto block traffic from a suspicious IP address.

TheData Inventoryfeature in Splunk Security Essentials provides analysts with a comprehensive listing of all currently onboarded data sources within the Splunk environment. This enables users to quickly identify what data is available for security use cases, allowing targeted viewing and analysis based on the indexed datasets.

Data Inventoryconsolidates metadata about indexes, sourcetypes, and data volume, helping analysts understand their security coverage.

Security Data Journeyis a guided workflow for progressing through security content and maturity but does not specifically list data sources.

Security Contentrefers to detection content like correlation searches, dashboards, and reports.

Data Source Onboarding Guideshelp users onboard new data sources but do not reflect the current onboarded data inventory.

TheSplunk Security Essentials documentationhighlights Data Inventory as a key tool for bridging visibility gaps and accelerating content deployment aligned with available data.

Under the General Data Protection Regulation (GDPR), Personal Data is any information relating to an identified or identifiable natural person. An individual's address, combined with their first and last name, clearly identifies a person, making it Personal Data under GDPR. The other options provided do not meet the GDPR criteria for Personal Data: the birth date of an unidentified user does not identify a person, the name of a deceased individual is not covered under GDPR, and a company’s registration number pertains to an entity rather than a natural person.

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Splunk Enterprise Security provides a feature calledFramework Mappingthat allows correlation searches to be mapped to specific cybersecurity frameworks, including NIST 800-171, which is crucial for DoD contractors. This mapping provides context to the analyst by showing how particular searches align with compliance requirements, aiding in continuous monitoring and reassessment as mandated by the DoD. This feature is integral for organizations that need to demonstrate compliance with NIST guidelines and other security frameworks.

TheContinuous Monitoringcycle begins with theDefine and Predictphase, where the security team establishes what needs to be monitored (defining assets, risks, controls) and predicts potential threats and vulnerabilities. This foundational phase sets objectives and scope for the monitoring program.

Subsequent phases likeMonitor and Protect,Assess and Evaluate, andRespond and Recoverfollow the initial definition.

This phased approach aligns with frameworks such as NIST’s Risk Management Framework and Continuous Monitoring guidelines.

Splunk’s cybersecurity documentation highlights the importance of this first phase for establishing a proactive and informed security posture.

An event where a user authenticates from geographically distant locations within an impossibly short timeframe is classified as anAccess Anomaly. This type of anomaly reflects unusual or suspicious access behavior that deviates from normal user patterns.

Access Anomaliestypically focus on login behavior, including impossible travel, unusual times, or atypical locations.

Identity Anomaliesrelate to suspicious behavior linked to user identities but are broader than access specifics.

Endpoint Anomaliesfocus on device or host irregularities.

Threat Anomaliesrefer to indications of malicious activity detected by threat intelligence or detection content.

Splunk’sEnterprise Security documentationcategorizes impossible travel as a classic Access Anomaly, frequently flagged by correlation searches and triggering notable events.

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.

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.

Continuous Monitoring Cycle:This cycle is part of a broader security strategy that involves constantly assessing and managing the security state of an organization's information systems. The phases generally include defining metrics, collecting data, analyzing it, reporting findings, and implementing improvements.

Analyze and Report Phase:

Data Evaluation:In this phase, the data collected from various monitoring tools and sensors is thoroughly analyzed. Security analysts look for trends, anomalies, and indications of potential threats or vulnerabilities.

Reporting:After the analysis, a report is generated that highlights the findings, including any detected issues, their potential impact, and the current effectiveness of security measures.

Recommendations:Based on the analysis, the report usually includes suggestions for improvements, such as additional security controls, configuration changes, or policy updates. These recommendations are aimed at enhancing the organization's security posture and addressing any identified gaps or weaknesses.

Purpose of Recommendations:The goal of this phase is to ensure that the organization’s security measures are continuously improved based on the latest data and threat landscape. It is a critical step in maintaining an effective security program that adapts to new challenges.

In Splunk, therarecommand is used to return the least common values in a field. This command is particularly useful for anomaly detection, as it helps identify unusual or infrequent occurrences in a dataset, which may indicate potential security issues.

rare Command:

This command works by identifying values that appear infrequently within a specified field. It’s a powerful tool for Cyber Defense Analysts who are looking for anomalies that could signify malicious activities.

Incorrect Options:

A. least:This is not a valid Splunk command.

B. uncommon:This is not a valid Splunk command.

D. base:This is not a relevant command for finding the least common values.

In Splunk, when an analyst is building a search and finds that extracted fields are not appearing, it often relates to the search mode being used.Smart ModeorVerbose Modeare better suited for field extraction as they allow Splunk to automatically extract and display fields based on the data being searched.

Search Modes in Splunk:

Fast Mode:Optimizes search performance by limiting field extractions to only those required by the search. If the analyst is in Fast Mode, non-required fields may not be extracted or displayed.

Smart Mode:Balances performance and field extraction, allowing fields to be automatically extracted and made available for analysis.

Verbose Mode:Extracts all fields and provides the most complete view of the data, though it may be slower.

Incorrect Options:

A. The analyst does not have the proper role to search this data:If this were the case, the analyst might not be able to search at all, rather than just missing extracted fields.

B. The analyst is searching newly indexed data that was improperly parsed:This would likely lead to no data being returned, rather than just missing fields.

C. The analyst did not add the extract command to their search pipeline:Field extraction in Splunk is usually automatic unless specific commands are used; the issue here is more likely related to search mode.

Thetstatscommand in Splunk is designed for highly efficient querying of large datasets because it operates primarily onindexed metadatarather than the full raw event data. Indexed metadata consists of pre-extracted, summarized information such as field values, timestamps, and host information, which is stored in a highly optimized format. This allowststatsto return results much faster than thestatscommand, which processes raw event data after retrieval.

tstatsqueries accelerated data models or index metadata directly, reducing disk I/O and CPU usage.

statsoperates on raw events, which can be expensive in terms of resources for large datasets.

The SQL-like syntax oftstats(Option C) is a convenience but not the reason for its performance benefits.

tstatsdoes not search raw logs for extracted fields; that is the domain of thestatscommand.

Splunk’s official documentation and theCybersecurity Defense Analyst Study Guideemphasize the importance oftstatsfor performance-optimized queries in security operations and large-scale data analysis.

In theSplunk Common Information Model (CIM), the Authentication Data Model defines standardized field names to normalize data from various sources. For privilege escalation events, the user who initiates the escalation (i.e., the actor performing the elevated action) is represented by the fieldsrc_user. This field captures the source or originating user account involved in the authentication event.

src_user: Typically refers to the user who initiated the action, such as the account escalating privileges.

dest_user: Refers to the target user account involved, such as the user being impersonated or the elevated account.

src_user_idandusernameare either deprecated or less precise in CIM for privilege escalation contexts.

TheSplunk CIM documentation(specifically the Authentication Data Model section) states thatsrc_useris the correct normalized field to capture the initiating user in escalation or impersonation events, supporting consistent searches, alerts, and dashboards across varied log sources.

An executable running from theC:\Windows\Tempdirectory is a significant red flag because temporary directories are often world writable, meaning any user or process can write files to them. This characteristic makes these directories an attractive target for attackers who want to drop, stage, and execute malware without worrying about restrictive file permissions.

Temp Directories Characteristics:

World Writable:Temporary directories likeC:\Windows\Tempare typically writable by all users, which reduces the complexity for an attacker to place and execute malicious files.

Lack of Permissions Control:Since these directories do not have strict permission controls, attackers can easily exploit them to execute malicious payloads without being hindered by file access restrictions.

Security Risks:

Malware Staging:Attackers often use temp directories to stage malware because they can avoid detection by traditional security controls that monitor more critical directories like system folders.

Persistence Mechanisms:Some malware will persist in these directories and execute from them each time the system starts or when a particular trigger is activated.

Privilege Escalation:In some cases, malicious files placed in temp directories can be executed by more privileged processes, leading to privilege escalation.

Investigation Importance:The fact that an executable is running fromC:\Windows\Tempwarrants further investigation to determine whether it is malicious. Analysts should check:

File Origin:How the file got there, which user or process created it.

Behavior:What the executable is doing, such as establishing network connections, modifying system settings, or interacting with other sensitive files.

Integrity:Whether the executable is a legitimate system file or a potential piece of malware.

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.

TheInvestigation Managementframework in Splunk Enterprise Security (ES) is specifically designed to help analysts manage the lifecycle of security incidents. This framework allows for the identification of incidents from aggregated notable events, and provides tools to manage incident ownership, track the triage process, and monitor the state or status of ongoing investigations.

Investigation Managementoffers workflows to assign incidents, add comments, document findings, and close investigations, ensuring a structured response process.

TheNotable Eventframework refers to the generation of alerts based on correlation searches but does not provide incident lifecycle management.

Asset and Identityenrich data with contextual information but is not focused on incident management.

Adaptive Responserefers to automated actions but not the management framework itself.

Splunk’sEnterprise Security User Guidedetails Investigation Management as the central tool for orchestrating SOC response workflows and ensuring effective case management.

The examples provided correspond to Tactics, Techniques, and Procedures (TTPs) in the following order:

Lateral movement– This is aTactic. Tactics represent the goals or objectives of an adversary, such as moving laterally within a network to gain broader access.

Exploiting a remote service– This is aTechnique. Techniques are specific methods used to achieve a tactic, such as exploiting a service to move laterally.

Use EternalBlue to exploit a remote SMB server– This is aProcedure. Procedures are the detailed steps or specific implementations of a technique, such as using the EternalBlue exploit to target SMB vulnerabilities.

Thus, the correct order isTactic, Technique, Procedure.

In this scenario, the analyst cannot conclude whether the Notable Event is a true positive or a false positive due to the absence of necessary logs and artifacts. The appropriate event disposition in this case is "Other," as it indicates that further action is required, such as ingesting the missing logs. The involvement of a security engineer to ensure the necessary data is available for proper investigation is implied, making "Other" the most suitable option.

In an organization, theSecurity Architectis typically responsible for designing new processes or selecting the tools necessary to protect assets that are identified as being at risk. The Security Architect has the expertise to design a comprehensive security solution that addresses the specific needs of the organization, considering various factors like existing infrastructure, threat landscape, and compliance requirements. They work closely with other roles, such as Security Engineers, to implement these solutions.

In the context of theTTP (Tactics, Techniques, and Procedures)framework used in cybersecurity, the termProcedurerefers to the specific implementation or method used by an adversary to execute an attack or achieve an objective. Here, "LoudWiner" represents a particular malware or tool used by the adversary to hijack resources for crypto mining, which is an actualprocedure—the concrete steps or methods taken to accomplish the broader technique or tactic.

Tacticis the adversary’s goal or objective (e.g., resource hijacking for financial gain).

Techniqueis a general method used to achieve the tactic (e.g., crypto mining via malware).

Procedureis the specific variant or implementation of the technique (e.g., using LoudWiner malware).

Problemis not a recognized element in the TTP framework.

TheMITRE ATT&CK frameworkdefines procedures as real-world implementations of techniques by adversaries. Splunk’s cybersecurity analyst materials emphasize understanding these distinctions to better map observed adversary behavior.

The main difference between hypothesis-driven and data-driven threat hunting lies in the approach. Inhypothesis-drivenhunting, the hunter starts with a theory or hypothesis about what kind of malicious activity might be occurring and then searches the data to confirm or refute that hypothesis. On the other hand,data-drivenhunting involves sifting through existing datasets to uncover patterns, anomalies, or activities that were not initially suspected. Hypothesis-driven approaches are more focused and often guided by threat intelligence or knowledge of attacker behaviors, while data-driven approaches rely on broad data analysis to identify unexpected threats.

The Splunk Security Content library, which includes apps like ESCU (Enterprise Security Content Update) and SSE (Splunk Security Essentials), primarily consists of Dashboards, Reports, and Correlation Searches.Validated architecturesare not a component of these content libraries. Instead, validated architectures refer to predefined, best-practice designs for deploying and configuring Splunk in a way that ensures optimal performance and scalability, which is separate from the content libraries focused on delivering security detections and visualizations.

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Data Model Acceleration (DMA)in Splunk is used to improve search performance by creating summarized, indexed versions of data models that enable much faster retrieval than querying raw indexed data directly. This acceleration is particularly beneficial for complex searches, dashboards, and reports based on large datasets.

DMA precomputes and stores data aggregates, allowing near real-time responses for repeated queries.

It does not normalize data—that function is handled by CIM and data models themselves.

DMA is unrelated to comparing algorithms or modeling response actions.

According toSplunk documentation, enabling acceleration on data models significantly reduces search latency and enhances user experience, especially in security operations with high volumes of data.

In Splunk, field extractions are essential for transforming raw log data into structured fields that are easier to work with during analysis. When the question refers to an analyst identifying helpful information in the raw logs that assists them in determining suspicious activity, the most effective way to streamline this process is throughfield extraction. This allows the Splunk system to automatically parse and tag the necessary data, making it more accessible for searches, dashboards, and alerts.

Let’s break down whyoption A: Create a field extraction for this informationis the best approach:

Field Extraction Overview:

Field extraction is a process within Splunk that takes unstructured log data and converts it into structured fields.

This makes it possible to directly query and display these fields, allowing analysts to quickly find and use relevant data in their investigations.

For example, if the logs contain IP addresses, user IDs, file names, or activity types, extracting these fields enables the analyst to filter and correlate data much more effectively without manually scanning the raw logs.

Why Field Extraction?

In this case, the question suggests that the raw logs contain information that helps determine whether activity is malicious. By creating field extractions for the relevant data points, analysts can use those structured fields to build queries and visualizations, drastically speeding up analysis time.

Analysts can write custom Splunk queries to isolate events that meet specific conditions, such as matching specific cloud sharing activities associated with risk notables.

Field extraction improves not only real-time analysis but also supports retrospective analysis and incident correlation across multiple events.

Comparison to Other Options:

Option B: Add this information to the risk message– While adding more context to a risk message could be useful for reviewing individual alerts, it doesn’t improve the efficiency of log analysis. The analyst still would need to go back and manually inspect raw logs for more detailed data.

Option C: Create another detection for this information– Creating additional detections adds more rules, but doesn't solve the fundamental issue of having raw logs that aren’t easily searchable. You can only build effective detections when you have structured data available.

Option D: Allowlist more events based on this information– Allowlisting is generally used to reduce noise or irrelevant logs, but it doesn't help extract the necessary details for analysis. It may reduce unnecessary alerts, but won’t help analyze the suspicious events that do arise.

Cybersecurity Defense Analyst Best Practices:

Field extractionsshould be created for any important log source or data point, especially when handling complex or multi-part log entries (e.g., cloud sharing logs). This ensures logs are searchable and actionable, allowing for faster identification of anomalies and malicious activity.

Analysts should collaborate with engineers to ensure these extractions are tuned and validated. The extraction should be tailored to isolate the fields most relevant for identifying suspicious activity.

Once fields are extracted, analysts can create dashboards, real-time alerts, or retrospective searches based on the structured data for more effective incident response.

The Lockheed Martin Cyber Kill Chain® is a widely recognized framework that breaks down the stages of a cyber attack. The stages are: Reconnaissance, Weaponization, Delivery, Exploitation, Installation, Command and Control (C2), and Actions on Objectives. The scenario described—modifying the registry on a compromised Windows system to ensure malware runs at boot time—fits into theInstallationphase. This phase involves placing a persistent backdoor or other malicious software on the victim's system, ensuring it can be executed again, even after a system reboot. By modifying the registry, the attacker is achieving persistence, a classic example of the Installation phase.