SecurityX CAS-005 CompTIA Study Notes

In the given scenario, the security engineer is likely examining login activities and their associated geolocations. This type of analysis is aimed at identifying unusual login patterns that might indicate an impossible travel scenario. An impossible travel scenario is when a single user account logs in from geographically distant locations in a short time, which is physically impossible. By assessing login activities using geolocation, the engineer can tune alerts to identify and respond to potential security breaches more effectively.

The Common Vulnerability Scoring System (CVSS) v3.1 uses three metric groups to calculate overall scores:Base,Temporal, andEnvironmental.

Base (E):Mandatory metrics assessing exploitability (e.g., attack vector) and impact (confidentiality, integrity, availability).

Temporal (A):Optional metrics reflecting the current state of the vulnerability (e.g., exploit availability, remediation level).

Environmental (F):Optional metrics tailoring the score to the organization’s context (e.g., security requirements).

B, C, D (Availability, Integrity, Confidentiality):These are subcomponents of the Base Impact metrics, not standalone groups.

G (Impact):A categorywithin Base, not a group.

H (Attack vector):A single Base metric, not a group.

Tokenizationreplaces sensitive data (e.g., PII) with non-sensitive placeholders while maintaining format consistency, ensuring compliancewithout disrupting testing. This method is commonly used forPCI-DSS and GDPR compliancewhile preserving data structure for functional tests.

Encryption (A)secures data but does not remove sensitivity or solve testing concerns.

Truncation (B)removes portions of data but may impact testing if format requirements are strict.

Synthetic data (C)can be useful but may not always match real-world scenarios perfectly for testing purposes.

Tokenization replaces sensitive data elements with non-sensitive equivalents, called tokens, that can be used within the internal tests. The original data is stored securely and can be retrieved if necessary. This approach allows the software development team to work with data that appears realistic and valid without exposing the actual sensitive information.

Configuring data hashing (Option A) is not suitable for test data as ittransforms the data into a fixed-length value that is not usable in the same way as the original data. Replacing data with null records (Option C) is not useful as it does not provide valid data for testing. Data obfuscation (Option D) could be an alternative but might not meet the regulatory requirements as effectively as tokenization.