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| Section | Weight | Objectives |
|---|---|---|
| Securing AI Systems | 40% | - AI System Protection
|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI Threat Landscape
|
| AI-Assisted Security | 24% | - Security Operations Enhancement
|
| AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
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NEW QUESTION # 144
An organization is developing and implementing AI features into a customer service application.
Which of the following practices should the organization put in place before releasing the application for customer trials?
Answer: B
Explanation:
Basic Concept: Before deploying AI applications that handle customer data in trials, protecting sensitive information through data masking and sanitization is essential. CompTIA SecAI+ Study Guide emphasizes pre-deployment data security controls as a critical step in the AI development lifecycle.
Why A is Correct: Data masking replaces sensitive real customer data with realistic but fictitious equivalents, while sanitization removes harmful or unwanted data elements. Before customer trials, these techniques prevent exposure of real PII or sensitive information, ensure the trial environment cannot leak production data, and protect the organization from privacy regulation violations. This is the most immediately actionable pre-trial security control.
Why B is Wrong: External compliance audits are formal processes typically conducted post-deployment or at planned intervals to verify regulatory compliance. They are not pre-trial security implementations and cannot prevent data exposure in a trial environment.
Why C is Wrong: Approved AI vendor lists are governance artifacts that manage vendor selection risk at the procurement stage. They do not directly protect customer data within an application being prepared for trials.
Why D is Wrong: Third-party risk management addresses risks from external vendors and partners at a strategic level. While important for overall governance, it does not constitute a direct data security control for a pre-trial release.
NEW QUESTION # 145
Faculty members at a university are concerned about potential inherent bias and inconsistency in one department's AI plagiarism detection service.
Which of the following principles will most likely to address their concerns?
Answer: C
Explanation:
Consistency ensures that an AI system applies rules and produces results in a uniform manner across all cases. This principle directly addresses concerns about bias and irregular outcomes in the plagiarism detection service.
NEW QUESTION # 146
A critical AI system cannot be shut down and must remain secure. Which of the following actions should be performed to apply controls?
Answer: C
Explanation:
Option B is correct because patching critical vulnerabilities directly removes or reduces known exploitable weaknesses while allowing the essential AI service to remain available. The organization should prioritize patches according to exploitability and impact, test them in a representative environment, use redundancy or rolling deployment where possible, and verify that remediation succeeded. Option A would weaken confidentiality and create an additional security exposure; encryption is not the obstacle to secure continuous operation. Option C is valuable as a detective control because log analysis can reveal abnormal activity, but it does not remediate a known critical vulnerability. Option D redeploys the production model, which may restore model files or configuration, but it does not necessarily update the vulnerable operating system, library, container, API, or orchestration component. For a system that cannot be shut down, the correct strategy is controlled patch management supported by high availability, maintenance coordination, monitoring, and rollback capability. NIST describes enterprise patch management as preventive maintenance that reduces the likelihood of compromise, breaches, and operational disruption.
NEW QUESTION # 147
During an update, an AI system flags some potential compatibility issues and provides recommendations. An administrator reviews the recommendations before addressing the issues.
Which of the following processes describes this scenario?
Answer: C
Explanation:
Basic Concept: Human-in-the-loop is a design pattern where AI systems generate recommendations or decisions but require human review and approval before those recommendations are acted upon. This approach maintains human oversight and accountability in AI-assisted workflows. CompTIA SecAI+ Study Guide covers human-in-the-loop as a key responsible AI principle and operational pattern.
Why C is Correct: The scenario precisely describes the human-in-the-loop pattern: the AI system identifies potential issues and provides recommendations, but an administrator must review those recommendations before any action is taken. This deliberate inclusion of human judgment in the AI ' s decision or recommendation workflow ensures human oversight is maintained, which is the defining characteristic of the human-in-the-loop process.
Why A is Wrong: Data validation verifies that data meets expected quality standards and formats before being used in AI processing. It is a data quality control activity, not a workflow pattern describing human review of AI recommendations.
Why B is Wrong: Data preparation involves transforming raw data into a format suitable for AI model training or inference. It encompasses cleaning, normalizing, and formatting data, not the process of human review of AI-generated recommendations during system updates.
Why D is Wrong: Model evaluation assesses a model ' s performance against metrics such as accuracy, precision, and recall on test datasets. It is a technical assessment of model quality, not a workflow process where humans review AI-generated recommendations before acting on them.
NEW QUESTION # 148
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 # 149
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