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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI-Assisted Security | 24% | - Security Operations Enhancement
|
| Topic 2: AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| Topic 3: Securing AI Systems | 40% | - Adversarial Defense
|
| Topic 4: Basic AI Concepts Related to Cybersecurity | 17% | - AI Threat Landscape
|
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NEW QUESTION # 78
A legal firm permits employees to use a company-authorized chatbot for their research and reporting. In the past year, the firm had to retract some of its released documents because of incorrect references.
Which of the following best describes the weakness that caused this issue?
Answer: A
Explanation:
Overreliance occurs when users place excessive trust in AI-generated output without performing sufficient independent verification. The scenario indicates that employees used a chatbot for research and reporting and subsequently published documents containing incorrect references. That pattern strongly indicates that AI- generated citations or factual claims were accepted without adequate human validation. CompTIA SecAI+ explicitly lists overreliance among attacks and weaknesses that security professionals must recognize when analyzing AI systems. Generative AI systems can produce hallucinated or inaccurate citations that appear credible, which is why human oversight and human validation remain important lifecycle controls. Insecure output handling instead concerns unsafe downstream processing of model output, such as executing generated code or passing untrusted output into another system without validation. Excessive agency occurs when an AI system has more autonomous permissions or capabilities than necessary. Sensitive information disclosure involves exposing protected or confidential information. None of those conditions explains publication of believable but inaccurate references as directly as overreliance. Appropriate mitigation includes independent verification, authoritative-source validation, human review, and monitoring AI output for accuracy and hallucinations before publication.
NEW QUESTION # 79
A security administrator must provide access controls for AI systems to list tables.
Which of the following should the administrator implement?
Answer: C
Explanation:
Basic Concept: AI systems interact with different resource layers including models, data stores, and infrastructure. Controlling what data an AI system can access requires implementing access controls at the data layer. CompTIA SecAI+ Study Guide differentiates between model access, data access, and network access controls for AI systems.
Why D is Correct: Data access controls govern what data resources an AI system can interact with, including which databases, tables, and records it can read or modify. To control an AI system ' s ability to list database tables, the administrator must implement data access controls that define precisely which tables the AI can enumerate and query, following the principle of least privilege for data interactions.
Why A is Wrong: Agentic AI access refers to permissions granted to autonomous AI agents to perform actions and use tools. It is a broader concept about what an AI agent can do operationally rather than a specific data-layer access control mechanism.
Why B is Wrong: A Network Access Control List controls network traffic at the IP and port level, determining which hosts can communicate with which network resources. It operates at the network layer and cannot enforce fine-grained control over which database tables an AI system is allowed to list.
Why C is Wrong: Model access controls govern who and what can interact with the AI model itself - who can query it, update it, or access its parameters. This is distinct from data access, which controls what the model can read from data stores during operation.
NEW QUESTION # 80
During the selection of a machine learning (ML)-based threat classification model, a cybersecurity administrator verifies that label distribution is highly unbalanced. Which of the following processing techniques should the engineer use to balance the model?
Answer: B
Explanation:
When label distribution is highly unbalanced, data augmentation generates additional synthetic samples for the underrepresented classes. This balances the dataset, improving the ML model's ability to classify threats accurately across all categories.
NEW QUESTION # 81
A financial services company is being sued for using AI in the loan underwriting process. The lawsuit reveals that the AI model used biased sources to disproportionately deny loans to residents in certain postal codes.
Which of the following employees is the most qualified to remove these biased sources from the model?
Answer: B
Explanation:
The data scientist is the most appropriate role because correcting biased source data requires examining the datasets used to develop, train, validate, or refine the model and then modifying the data preparation or model- development process. Data scientists work directly with training data, feature selection, statistical distributions, model development, and evaluation, which places them in the strongest position to identify biased inputs and retrain or adjust the model using more representative information. CompTIA SecAI+ includes the data scientist as a formal AI governance role and separately identifies fairness, introduction of bias, accuracy, and model performance as key responsible-AI risks. A platform engineer primarily manages the infrastructure supporting AI workloads. A cybersecurity analyst focuses on security threats, monitoring, and defensive controls rather than correcting discriminatory training data. An AI architect designs the broader architecture and integration of AI solutions but normally does not perform the detailed dataset preparation and model-training work required here. Because the problem originates in biased data sources, the data scientist is the role closest to the underlying cause.
NEW QUESTION # 82
Which of the following is the most impactful security risk associated with the use of a generative AI chatbot?
Answer: A
Explanation:
Basic Concept: Generative AI chatbots interact with users in natural language and may access organizational knowledge bases, databases, or prior conversations. The conversational nature of these systems creates unique risks around sensitive information disclosure. CompTIA SecAI+ Study Guide ranks data leakage as the primary security concern for generative AI chatbots.
Why B is Correct: Data leakage occurs when a generative AI chatbot inadvertently reveals sensitive information including PII, confidential business data, intellectual property, training data, or system configurations in its responses. This can happen through prompt injection attacks, insufficient output filtering, or the model memorizing and reproducing sensitive training data. The impact is immediate, potentially irreversible, and can result in regulatory violations, competitive disadvantage, and reputational damage.
Why A is Wrong: Overly permissive access is a contributing factor that can exacerbate data leakage but is an access control design issue rather than the most directly impactful runtime risk of operating a generative AI chatbot.
Why C is Wrong: Weak encryption is a data protection concern for data in transit or at rest. While important, it is a configuration issue separate from the generative AI chatbot ' s core operational risks and is not specific to chatbot technology.
Why D is Wrong: Model validation ensures a model performs as expected before deployment. While important for quality assurance, it is a development lifecycle activity rather than an ongoing operational security risk associated with running a chatbot.
NEW QUESTION # 83
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