CAS-005 Exam Dumps : CompTIA SecurityX Certification Exam

When using AI to fully automate the process of deciding client loan rates, the primary concern from a privacy perspective is model explainability.

Why Model Explainability is Critical:

Transparency: It ensures that the decision-making process of the AI model can be understood and explained to stakeholders, including clients.

Accountability: Helps in identifying biases and errors in the model, ensuring that the AI is making fair and unbiased decisions.

Regulatory Compliance: Various regulations require that decisions, especially those affecting individuals' financial status, can be explained and justified.

Trust: Builds trust among users and stakeholders by demonstrating that the AI decisions are transparent and justifiable.

Other options, such ascredential theft, prompt injections, and social engineering, are significant concerns but do not directly address the privacy and fairness implications of automated decision-making.

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.