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 best solution is to implement an interactive honeypot (A). Honeypots are decoy systems designed to attract and observe adversary behavior in real time. When security tools cannot categorize suspicious inbound traffic, a honeypot provides an isolated environment where the suspicious activity can be redirected and monitored without risking production systems. By deploying an interactive honeypot, analysts can study attacker tactics, techniques, and procedures (TTPs), extract Indicators of Compromise (IoCs), and improve defensive controls.

Option B (mapping to known IoCs) fails because the activity cannot be categorized, implying it is novel or not yet identified in threat intelligence feeds. Option C (monitoring the dark web) provides intelligence about potential threats but does not address real-time inbound suspicious activity. Option D (UEBA) focuses on analyzing user and entity behaviors but is less effective for categorizing inbound external traffic.

By using honeypots, organizations gain visibility into new, unknown, or advanced attack techniques, which helps improve detection capabilities, enrich threat intelligence, and strengthen incident response.

The event timeline indicates a sequence where a file (hr-reporting.docx) was saved, scanned, executed, and eventually found to contain malware. The critical issue here is that the malware scan completed after the file was already executed. This suggests a Time-Of-Check to Time-Of-Use (TOCTOU) vulnerability, where the state of the file changed between the time it was checked and the time it was used.