The answer is user and entity behavior analytics (UEBA). UEBA is the correct choice because it leverages machine learning and statistical models to establish a baseline of normal user behavior, such as typical login locations, times, and access patterns. When the employee’s credentials are used from a foreign IP address while the employee is physically in the office, UEBA detects this as an anomalous deviation from the established baseline, triggering a real-time alert. On the Systems Security Certified Practitioner SSCP exam, this question tests your understanding of risk identification techniques that rely on behavioral baselines rather than static rules; a common trap is confusing UEBA with signature-based intrusion detection systems, which cannot flag novel geolocation anomalies. Remember the memory tip: “UEBA sees the unusual—if the user’s habits break the baseline, UEBA will raise the sign.”
SSCP Risk Identification, Monitoring and Analysis Practice Question
This SSCP practice question tests your understanding of risk identification, monitoring and analysis. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A security team discovers that an employee's credentials were used to access the HR database from an unrecognized IP address in a foreign country. The employee is currently in the office. Which risk identification technique is most directly responsible for detecting this anomaly?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
User and entity behavior analytics (UEBA)
UEBA is the correct answer because it uses machine learning and statistical models to establish a baseline of normal user behavior (e.g., typical login times, geolocations, and access patterns). When the employee's credentials are used from a foreign IP address while the employee is physically in the office, UEBA detects this as an anomalous deviation from the baseline, triggering an alert. This technique is specifically designed for real-time anomaly detection in user and entity activities, making it the most direct method for identifying this type of credential misuse.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
User and entity behavior analytics (UEBA)
Why this is correct
UEBA detects deviations from normal behavior, such as login from unusual location.
Related concept
Read the scenario before looking for a memorised answer.
✗
Manual log review
Why it's wrong here
Manual review might detect it, but the question implies automated detection; UEBA is the typical technique.
✗
Vulnerability scanning
Why it's wrong here
Vulnerability scanning identifies technical weaknesses, not behavioral anomalies.
✗
Threat intelligence feeds
Why it's wrong here
Threat intelligence provides external threat data, not internal behavioral analysis.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse threat intelligence feeds (Option D) with anomaly detection, assuming that an unrecognized foreign IP would be flagged by a threat feed, but UEBA is the only technique that directly detects behavioral anomalies without relying on known-bad indicators.
Detailed technical explanation
How to think about this question
UEBA typically ingests logs from sources like Active Directory, VPN gateways, and web proxies, then applies algorithms such as clustering (e.g., k-means) or time-series analysis to model user behavior. For example, it might flag a login from a foreign IP if the user's previous 90-day geolocation history shows only local office access, even if the foreign IP is not on any blacklist. In real-world deployments, UEBA can also detect lateral movement by correlating failed logins with subsequent successful access to sensitive databases, reducing false positives through peer-group analysis.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A security team runs a vulnerability scan on a web application and discovers an unpatched SQL injection flaw. The team prioritises remediation by CVSS score — critical flaws are patched within 24 hours, high within 7 days. Questions like this test whether you understand vulnerability management processes, scanning tools, and remediation prioritisation.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Risk Identification, Monitoring and Analysis — This question tests Risk Identification, Monitoring and Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: User and entity behavior analytics (UEBA) — UEBA is the correct answer because it uses machine learning and statistical models to establish a baseline of normal user behavior (e.g., typical login times, geolocations, and access patterns). When the employee's credentials are used from a foreign IP address while the employee is physically in the office, UEBA detects this as an anomalous deviation from the baseline, triggering an alert. This technique is specifically designed for real-time anomaly detection in user and entity activities, making it the most direct method for identifying this type of credential misuse.
What should I do if I get this SSCP question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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