CAS-005 Exam Dumps : CompTIA SecurityX Certification Exam

The logs indicate multiple failed login attempts for user10, who may have been part of the staff reduction 60 days prior. If user10 ' s account was removed, and their home directory deleted, any applications or services relying on files or configurations within that directory would fail. This scenario is common when service accounts are not properly identified and preserved during staff reductions.

Ensuring that service accounts are documented and maintained separately from user accounts is essential to prevent unintended disruptions to applications and services.

The best solution to address reported vulnerabilities in third-party libraries is integrating a Static Application Security Testing (SAST) tool as part of the development pipeline. Here’s why:

Early Detection: SAST tools analyze source code for vulnerabilities before the code is compiled. This allows developers to identify and fix security issues early in the development process.

Continuous Security: By integrating SAST tools into the CI/CD pipeline, the organization ensures continuous security assessment of the codebase, including third-party libraries, with each code commit and build.

Comprehensive Analysis: SAST tools provide a detailed analysis of the code, identifying potential vulnerabilities in both proprietary code and third-party dependencies, ensuring that known issues in libraries are addressed promptly.

The best answer is C. Correlating based on source field in batches of time . To identify trends in SIEM data, the analyst should group related events over a time window and correlate them around a common field. In this excerpt, user1 appears repeatedly across multiple alert types, including unusual authentication, access to sensitive resources, and elevated risk score. That pattern is exactly the sort of repeated behavior that becomes visible when events are correlated by source over time. CompTIA’s official SecurityX objectives in the Security Operations domain include “Data analysis: indicators of malicious activity, trend analysis, statistics, and deduplication/correlation.” That objective language directly supports this answer.

Why the other options are not best:

A narrows only one rule window and does not systematically identify trends across multiple event types. B may reduce volume, but reduction alone does not reveal trends. D is risky because noisy rules might still provide useful context, and disabling them based only on total daily alerts can hide meaningful multi-event patterns. The practical, operational ap proach is to correlate records by a shared field such as source/user across time batches so repeated suspicious behavior stands out.