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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI-assisted Security | 24% | - AI in security strategy and operations
|
| Topic 2: Securing AI Systems | 40% | - Security controls for AI systems
|
| Topic 3: AI Governance, Risk and Compliance | 19% | - Risk management for AI
|
| Topic 4: Basic AI Concepts Related to Cybersecurity | 17% | - AI-driven threats and risks
|
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NEW QUESTION # 99
A security analyst is aware of an active penetration test in the environment. The analyst examines SIEM log data and notices the following AI system output:
Which of the following is the vulnerability that has occurred and the control the analyst should implement?
Answer: B
Explanation:
Basic Concept: AI systems can inadvertently reveal sensitive information such as PII, credentials, or internal data in their outputs when not properly controlled. Sensitive information disclosure is a critical OWASP LLM Top 10 risk. CompTIA SecAI+ Study Guide covers both vulnerability identification and appropriate data protection controls for AI outputs.
Why D is Correct: The scenario describes the AI system outputting sensitive information in its responses, which is a sensitive information disclosure vulnerability. The appropriate control is masking, which replaces sensitive data values such as credit card numbers, SSNs, or API keys with redacted or tokenized equivalents in the model ' s outputs before they are returned to users. This prevents the AI from disclosing sensitive data while still providing useful responses.
Why A is Wrong: Prompt injection involves crafting inputs to override model instructions. If the penetration test revealed sensitive information, the primary vulnerability is the disclosure of that sensitive data, not the injection mechanism itself. EDR monitors endpoint behavior, not AI output content.
Why B is Wrong: Model hallucinations produce fabricated information rather than disclosing real sensitive data. The described scenario involves actual sensitive information being revealed, not fictitious content generation.
Why C is Wrong: Jailbreaking circumvents safety restrictions but the primary harm demonstrated is sensitive data exposure. RBAC manages access permissions but does not prevent the model from including sensitive data in responses once access is granted.
Why E is Wrong: Role impersonation involves the AI pretending to be a different entity. This may be a secondary technique used by the penetration tester but the primary vulnerability described is the disclosure of actual sensitive information in the output.
NEW QUESTION # 100
A cyberthreat intelligence (CTI) analyst conducts research about a threat during a recent attack. The analyst uses an AI threat-modeling resource to find the relevant risks and utilizes causal and domain taxonomies.
Which of the following best describes which resource the analyst is using?
Answer: C
Explanation:
The MIT AI Risk Repository is specifically characterized by its use of both a Causal Taxonomy and a Domain Taxonomy. The repository consolidates AI risks from numerous frameworks and allows practitioners to classify and research risks according to how, when, and why they occur as well as the domain of harm involved. MIT states that its Causal Taxonomy addresses factors such as the responsible entity, intent, and timing, while its Domain Taxonomy categorizes risks into broader domains and subdomains. This terminology directly matches the scenario. CompTIA SecAI+ includes the MIT AI Risk Repository among the threat-modeling resources candidates must understand. MITRE ATLAS instead organizes adversarial AI activity into tactics, techniques, procedures, and case studies. The CVE AI Working Group focuses on vulnerabilities and related vulnerability-management concerns rather than causal/domain risk taxonomies.
OWASP ' s Machine Learning Security Top 10 concentrates on prominent ML security risks and defensive awareness. Because the question explicitly identifies causal and domain taxonomies, the MIT AI Risk Repository is the precise resource being described.
NEW QUESTION # 101
A security engineer needs to monitor an AI-based system for runtime operations. The engineer is mostly concerned about the visibility of internal activity. Which of the following is the most appropriate monitoring solution?
Answer: B
Explanation:
For runtime visibility into internal activity of an AI system, the most suitable control is enabling stack calls and debugging-level traces. This provides granular insights into function-level execution, dependencies, and operations, which directly supports monitoring of runtime behavior.
NEW QUESTION # 102
An IT company implements an adaptable chatbot that learns from user prompts. Based on the conversation shown - where User 2 injected false information about a company acquisition that caused the chatbot to give incorrect responses to User 3 - which of the following compensating controls should an administrator implement to mitigate the issue?
Answer: D
Explanation:
Basic Concept: A chatbot that learns from user prompts is vulnerable to data poisoning through conversational injection. Malicious users can deliberately introduce false information that the chatbot incorporates into its knowledge, corrupting responses for subsequent users. CompTIA SecAI+ Study Guide identifies this as a real-time data poisoning vector requiring guardrail controls.
Why D is Correct: Guardrails prevent the chatbot from accepting and incorporating unverified, irrelevant, or potentially malicious information injected by users. They enforce boundaries on what the chatbot can learn from user interactions, validate that information aligns with the system ' s purpose and known facts, and block outputs based on poisoned knowledge. Guardrails are specifically designed to prevent the type of conversational data poisoning demonstrated where a user ' s false claim corrupted the model ' s subsequent responses.
Why A is Wrong: Data encryption protects the confidentiality of data in transit and at rest. It does not prevent a chatbot from accepting and acting on false information that users deliberately inject into the conversation.
Why B is Wrong: API rate limiting restricts the frequency of requests. While it can limit the number of poisoning attempts a single user can make, it does not prevent the chatbot from learning from and propagating false information when requests are made at an acceptable rate.
Why C is Wrong: Transfer learning is a training technique that adapts knowledge from one domain to another.
It is a model development approach, not a runtime control that prevents users from injecting false information into a deployed chatbot.
NEW QUESTION # 103
An AI-based human resources system screens resumes to select the most suitable candidate for an interview.
The system recommends mostly male candidates. Which of the following responsible AI concepts does this system violate?
Answer: C
Explanation:
Fairness is the responsible AI principle most clearly violated because the system is producing a disproportionately gender-skewed outcome in a consequential employment decision. CompTIA SecAI+ explicitly identifies fairness as a responsible AI consideration and identifies the introduction of bias as an AI risk. A resume-screening system should evaluate candidates using relevant employment criteria without systematically disadvantaging a protected or unrelated demographic group. Recommending mostly male candidates signals a potential bias in the training data, feature selection, historical examples, model design, or decision threshold and therefore requires fairness assessment and mitigation. Transparency concerns whether stakeholders have appropriate visibility into how the AI system operates. Explainability concerns whether individual decisions can be described in understandable terms. Reliability concerns whether the system performs consistently and safely according to its intended specifications. Those properties remain important, but none directly describes the demographic disparity presented in the scenario. Because the evidence specifically indicates unequal treatment or outcomes between demographic groups, fairness is the responsible AI principle most directly implicated.
NEW QUESTION # 104
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