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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Securing AI Systems | 40% | - Adversarial Defense
|
| Topic 2: AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| Topic 3: Basic AI Concepts Related to Cybersecurity | 17% | - AI Threat Landscape
|
| Topic 4: AI-Assisted Security | 24% | - Security Operations Enhancement
|
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NEW QUESTION # 135
An AI security architect needs to conduct a security review of a new AI chatbot. Which of the following resources would the architect most likely rely on to perform a threat assessment and ensure comprehensive coverage during the review?
Answer: B
Explanation:
The OWASP Top 10 for Large Language Model Applications is the most directly applicable resource because the system under review is an AI chatbot based on LLM technology. CompTIA SecAI+ Objective 2.1 explicitly identifies the OWASP Top 10, including the LLM Top 10, as an AI threat-modeling resource. It addresses security risks specifically associated with LLM applications, including prompt injection, sensitive information disclosure, insecure output handling, excessive agency, and other AI-specific application threats.
ISO 27001 provides a broader information security management framework and is valuable for organizational security governance, but it is not specifically designed to enumerate LLM attack vectors. The EU AI Act primarily addresses regulatory obligations, risk classifications, and governance requirements rather than serving as a technical LLM threat-assessment checklist. Option A is also not the appropriate LLM-specific resource. For a security architect who needs systematic technical coverage of threats affecting an AI chatbot, the OWASP LLM Top 10 provides the most targeted framework among the listed options.
NEW QUESTION # 136
Which of the following resources most accurately covers the most widely recognized threat vectors and common vulnerabilities related to large language model (LLM) applications?
Answer: D
Explanation:
OWASP is the best answer because it publishes the Top 10 for Large Language Model Applications, a resource specifically structured around significant security vulnerabilities and attack patterns affecting LLM- based applications. CompTIA SecAI+ Objective 2.1 directly lists the OWASP Top 10 and specifically the LLM Top 10 as an AI threat-modeling resource. MITRE ATLAS is also highly relevant to AI security, but its purpose is broader: it catalogs adversarial tactics, techniques, and procedures associated with attacks against AI and machine learning systems. The MIT AI Risk Repository aggregates a broad range of AI risks rather than functioning as the recognized LLM application vulnerability Top 10. NIST AI RMF provides a risk- management framework for governing and managing AI risks but is not primarily a vulnerability catalog for LLM applications. Since the question asks for the resource that most directly captures widely recognized threat vectors and common vulnerabilities associated specifically with LLM applications, OWASP ' s LLM- focused Top 10 is the most precise match.
NEW QUESTION # 137
A security architect performs threat modeling of an AI system. The architect needs to determine which attacks can be performed against the system.
Which of the following actions should the architect take next?
Answer: A
Explanation:
MITRE ATLAS is specifically designed to catalog adversarial TTPs targeting AI systems. By analyzing ATLAS, the architect can determine which types of attacks are possible against the AI system, making it the most appropriate resource for threat modeling in this context.
NEW QUESTION # 138
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 # 139
An architect is creating a threat model for an agentic system.
Which of the following should the architect do first?
Answer: A
Explanation:
Basic Concept: Threat modeling for any system, and especially for agentic AI systems with multiple interacting components, begins with understanding the system ' s architecture and where trust boundaries exist. Trust boundaries define where data and control flows cross between components with different trust levels, representing potential attack surfaces. CompTIA SecAI+ Study Guide aligns with STRIDE and MITRE ATLAS threat modeling methodologies.
Why B is Correct: Identifying trust boundaries between components is the foundational first step in threat modeling. Agentic systems often involve multiple components such as the orchestrator, tools, APIs, data sources, and external services with different trust levels. Understanding where these boundaries exist reveals where untrusted inputs cross into trusted components, enabling the architect to systematically identify threats at each boundary before proceeding to risk quantification and control application.
Why A is Wrong: Applying compensating controls based on exposure findings is the final step in threat modeling, occurring after threats have been identified and risks quantified. Controls cannot be appropriately designed without first understanding the system ' s trust boundaries and threat landscape.
Why C is Wrong: Calculating risk to resources based on data sensitivity is a risk assessment step that occurs after trust boundaries are mapped and potential threats are identified. Risk quantification requires knowing what threats exist at each boundary first.
Why D is Wrong: Scanning for OWASP Top 10 vulnerabilities is a technical vulnerability assessment activity. While valuable, it comes after the architectural analysis of trust boundaries and threat identification phases of threat modeling.
NEW QUESTION # 140
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