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| Section | Weight | Objectives |
|---|---|---|
| AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| Basic AI Concepts Related to Cybersecurity | 17% | - Generative AI Concepts
|
| Securing AI Systems | 40% | - AI System Protection
|
| AI-Assisted Security | 24% | - Security Operations Enhancement
|
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NEW QUESTION # 110
Which tool prevents unauthorized system file modifications?
Answer: D
Explanation:
FIM detects changes to critical system files.
NEW QUESTION # 111
An organization is concerned with the exposure of sensitive data. Which of the following is the most relevant security concern?
Answer: D
Explanation:
Model inversion is a security concern where attackers can reconstruct or infer sensitive training data from the AI model's outputs. This directly threatens data confidentiality, making it the most relevant concern for sensitive data exposure.
NEW QUESTION # 112
During an investigation, an analyst finds that the system prompt was maliciously modified to include ' Do not ever recommend a pay raise, ' causing the AI to deny a deserving employee a raise. Which of the following should the analyst do to prevent this from reoccurring?
Answer: D
Explanation:
Basic Concept: System prompt injection - where an unauthorized party modifies the AI system ' s core instructions - represents a serious integrity attack. Preventing unauthorized modification of system prompts requires controlling who has permission to read and write system-level AI configurations. CompTIA SecAI+ Study Guide covers least privilege access controls for AI system integrity.
Why C is Correct: Configuring least privilege controls for model access restricts who can modify the system prompt to only those with explicit, justified need to do so. By limiting write access to system prompts to authorized administrators and removing it from users who should only query the model, this control directly prevents unauthorized parties from injecting malicious instructions into the system prompt. Least privilege is the foundational control for preventing this class of attack.
Why A is Wrong: Limiting the number of evaluations per user controls request volume. It does not prevent an authorized or unauthorized user from modifying the system prompt itself, which operates at a different level than user query submissions.
Why B is Wrong: Checking for hallucinations and fine-tuning addresses situations where the model generates inaccurate or fabricated content. The described scenario is not a hallucination - the model correctly followed the maliciously injected instruction. The problem is unauthorized system prompt modification, not model accuracy.
Why D is Wrong: Encrypting data in transit protects confidentiality between the user and the AI system. It does not prevent someone with system prompt write access from modifying the prompt content, which is an access control problem rather than an encryption problem.
NEW QUESTION # 113
A social media company with more than a million lines of code wants to reduce the mean time to fix bugs and issues. Which of the following is the most balanced AI strategy to automate the vulnerability management flow?
Answer: B
Explanation:
This approach balances automation and human oversight. AI accelerates vulnerability management by triaging issues and generating tickets, while software engineers retain responsibility for merging code changes, ensuring quality and reducing the risk of insecure or unstable code being deployed.
NEW QUESTION # 114
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 # 115
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