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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 Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| AI-Assisted Security | 24% | - Security Operations Enhancement
|
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問題 #128
For which of the following situations would a human-in-the-loop be most recommended?
答案：C
解題說明：
Agentic AI that can automatically patch organizational systems represents the highest-impact and highest-risk scenario, making human-in-the-loop control the most appropriate. CompTIA SecAI+ identifies human-in-the- loop, human oversight, and human validation as human-centric AI design principles. Agentic systems differ from passive AI because they can take actions through tools, APIs, scripts, or other integrated systems.
Automated patching can modify production hosts, restart services, introduce incompatibilities, cause outages, or affect business-critical applications. Requiring human authorization before consequential actions provides an important control point for validating the recommendation, affected assets, timing, dependencies, and rollback plan. A summarization model normally produces content for review without directly modifying systems. A graphics-generation model likewise creates content rather than performing privileged infrastructure changes. An FAQ chatbot is generally constrained to answering questions and typically has limited operational impact. Human oversight can still be valuable in all three situations, but the potential operational consequences are considerably greater when autonomous AI can change production systems.
Therefore, agentic AI performing patching is the scenario where human-in-the-loop governance is most strongly warranted.
問題 #129
Which of the following is used to train an AI model with unstructured data?
答案：A
解題說明：
Fine-tuning allows an AI model to adapt to unstructured data (such as text, audio, or images) by retraining it on domain-specific datasets. This process improves the model's ability to handle and generate outputs aligned with the unstructured data context.
問題 #130
A healthcare organization plans to deploy a chatbot for appointment scheduling and patient records. Which of the following is the first step a security administrator should take?
答案：A
解題說明：
Before deploying an AI chatbot that will handle sensitive healthcare data, the first step is to conduct a risk assessment. This identifies potential threats, compliance requirements (such as HIPAA), and security gaps, ensuring proper controls are planned before implementation.
問題 #131
Which of the following is an example of how a security analyst uses generative AI in the triage process?
答案：A
解題說明：
Basic Concept: Generative AI produces natural language content based on input data. In a security operations context, triage involves rapidly understanding and prioritizing security events. Generative AI ' s strength lies in synthesizing information and producing readable summaries from complex data. CompTIA SecAI+ Study Guide covers generative AI applications in security operations.
Why C is Correct: Summarizing security findings by category is a natural application of generative AI in triage. The AI can process large volumes of alerts and security events, group them by type or severity, and generate concise natural language summaries that enable analysts to quickly understand the current threat landscape without reading individual alerts. This directly reduces triage time and cognitive load.
Why A is Wrong: Predicting the next attack target requires predictive analytics and threat intelligence correlation. While AI can assist with this, it is a forecasting task better suited to analytical ML models rather than generative AI, and it is a strategic intelligence function rather than a triage task.
Why B is Wrong: Statistical analysis for malicious code assessment uses mathematical and ML techniques to analyze code characteristics. This is a traditional ML classification task, not a generative AI application, and is performed during malware analysis rather than alert triage.
Why D is Wrong: Tagging malware using ML algorithms is a classification task that uses supervised ML models trained on malware features. It is a detection and classification function, not a generative AI triage application.
問題 #132
An AI security team must assess the probability of an attack on its new system and the impact associated with such an attack.
Which of the following threat-modeling resources best addresses the threat landscape for machine learning (ML)?
答案：A
解題說明：
Basic Concept: Assessing attack probability and impact for ML systems requires a resource specifically built to catalog real-world adversarial attacks against AI and ML systems, including documented techniques with associated impact information. CompTIA SecAI+ Exam Objectives identify MITRE ATLAS as the authoritative ML threat landscape resource.
Why B is Correct: MITRE ATLAS is specifically designed as a comprehensive knowledge base of adversarial tactics, techniques, and case studies targeting AI and ML systems. It catalogs real-world attacks with associated probability factors derived from actual incidents and provides impact assessments for various attack types including data poisoning, model evasion, model extraction, and inference attacks. This directly enables the probability and impact assessment the team requires.
Why A is Wrong: The CVE AI working group focuses on identifying and cataloging specific vulnerability instances in AI software components. While useful for vulnerability management, it does not provide the comprehensive threat landscape coverage with probability and impact assessments for ML-specific attack tactics that ATLAS provides.
Why C is Wrong: The MIT risk repository is an academic resource cataloging general AI-related risks. It is research-oriented and does not provide the practitioner-focused, operational attack taxonomy and case study library that MITRE ATLAS offers for ML threat modeling.
Why D is Wrong: OWASP provides application security guidance including the OWASP LLM Top 10. While valuable for LLM-specific risks, OWASP does not provide the comprehensive ML threat landscape coverage or the probability and impact data that MITRE ATLAS offers for assessing the full spectrum of ML attack scenarios.
問題 #133
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