SPLK-5001 Exam Dumps - Splunk Certified Cybersecurity Defense Analyst

In Splunk’s Risk-Based Alerting (RBA) framework, aRisk Objectrefers to the specific entity (such as a user account, IP address, or host) that is associated with risk observations. When a user account generates multiple risk observations, it is labeled as a Risk Object, allowing security teams to track and manage risk more effectively.

Risk Object:

The Risk Object is central to Splunk’s RBA approach, which aggregates and evaluates risk across entities within an environment. This allows for a focused response to high-risk entities based on the accumulation of risk events.

Incorrect Options:

A. Risk Factor:This might refer to specific criteria or conditions that contribute to risk but does not denote the entity itself.

B. Risk Index:Could refer to a collection of risk-related data, not the specific entity.

C. Risk Analysis:Refers to the process of analyzing risk, not the entity under observation.

Abaselineis a representation or model of normal network activity, capturing expected patterns and behaviors over time. It serves as a reference point against which anomalies and deviations can be detected. By establishing a baseline, security analysts can identify unusual activities that might indicate security incidents.

Baselineinvolves statistical analysis of historical data to define normal ranges for metrics like traffic volume, connection rates, or login times.

Clusterrefers to grouping similar data points, often used in anomaly detection but not synonymous with a model of normal activity.

Time seriesis a sequence of data points indexed in time order, useful for monitoring trends but not specifically a model of normalcy.

Data modelrefers to an abstract schema for organizing data, such as Splunk’s CIM data models.

TheSplunk Cybersecurity Defense Analyst Study Guidehighlights baselining as a fundamental technique in anomaly detection and network security monitoring.

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.

In Splunk Enterprise Security, when assets are properly defined and enabled, the fieldsrc_categoryis automatically added to search results. This field categorizes the source IP addresses according to their asset classification, which helps in analyzing and filtering search results based on the type of assets involved in an event. Proper asset and identity management within Splunk ES enhances the ability to contextualize and prioritize security incidents.

The description outlines a process where similar data points (emails with similar attributes) are grouped based on their proximity in a multidimensional space (sender, recipient, subject, URLs, attachments), which is a classic definition ofclustering. Clustering algorithms group data points into clusters so that points in the same cluster are more similar to each other than to those in other clusters.

Clusteringis used extensively in threat hunting to identify campaigns or patterns that share common characteristics but may not be exactly the same, such as phishing emails with minor variations.

Least Frequency of Occurrence Analysisfocuses on rare events, which is opposite of the described method.

Time Series Analysisanalyzes data points in temporal order, not similarity grouping.

Most Frequency of Occurrence Analysiscounts most frequent items but does not spatially group them by similarity.

TheSplunk App for Data Science and Deep Learningsupports clustering techniques such as k-means, DBSCAN, and hierarchical clustering, which analysts can use to detect campaigns spreading across multiple users.