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| Section | Weight | Objectives |
|---|---|---|
| AI Governance, Risk and Compliance | 19% | - Risk management for AI
|
| AI-assisted Security | 24% | - Security automation and orchestration
|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI-driven threats and risks
|
| Securing AI Systems | 40% | - Security controls for AI systems
|
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NEW QUESTION # 53
A security operations center (SOC) has a very high volume of logs and alerts. The manager proposes the implementation of machine learning (ML) system to help with triage. Which of the following tasks is most suitable?
Answer: B
Explanation:
Machine learning is best suited for analyzing large volumes of security data and distinguishing between true threats and false positives. By identifying and classifying alerts, the ML system helps the SOC prioritize incidents and reduce analyst workload.
NEW QUESTION # 54
Which of the following attacks is most enabled by AI-generated content?
Answer: A
Explanation:
Basic Concept: AI-generated content including personalized text, synthetic voice, and deepfake video has dramatically enhanced the effectiveness and scalability of social engineering attacks. Understanding how AI amplifies specific attack types is key to CompTIA SecAI+ basic AI concepts in the cybersecurity context.
Why B is Correct: Phishing attacks are most dramatically enabled by AI-generated content. AI can generate highly personalized, grammatically perfect phishing emails tailored to individual targets using publicly available information. It can create convincing deepfake audio and video for voice phishing (vishing) and video phishing, replicate executive communication styles for business email compromise, and generate phishing campaigns at massive scale. The quality and personalization that previously required skilled human social engineers can now be automated with AI.
Why A is Wrong: Model poisoning is a specific attack against AI systems that corrupts training data to manipulate model behavior. While sophisticated, it is a targeted AI security attack rather than a broad cybercrime enabled by AI-generated content at scale.
Why C is Wrong: Ransomware is malware that encrypts victim data and demands payment for decryption keys. While AI can assist in ransomware development, ransomware deployment relies on code execution and network propagation techniques more than AI-generated content.
Why D is Wrong: Remote code execution involves exploiting vulnerabilities to run arbitrary code on a target system. It relies on technical vulnerability exploitation rather than AI-generated content. AI might assist in finding vulnerabilities, but RCE is not primarily enabled by content generation.
NEW QUESTION # 55
A data set containing medical information is put into a machine learning (ML) model that is designed to predict specific illnesses for a population. In the process of verifying the reliability of the system, the compliance officer realizes that the system cannot reliably predict illnesses for certain segments of the population.
Which of the following types of risk is most applicable to this case?
Answer: A
Explanation:
Basic Concept: AI models trained on unrepresentative data can produce systematically inaccurate results for certain population groups. This is a form of algorithmic bias where the model ' s performance varies significantly across demographic segments, creating disparate outcomes. CompTIA SecAI+ Exam Objectives cover bias as a core AI governance and risk concept.
Why A is Correct: Bias in AI occurs when a model produces systematically skewed results for certain groups due to biased training data, flawed data collection, or model design choices. In this healthcare scenario, the inability to reliably predict illnesses for specific population segments indicates the training data likely underrepresented those segments, causing the model to learn inadequate patterns for them. This is a critical bias risk with serious health equity implications.
Why B is Wrong: Consistency refers to the model producing the same output given the same input across different runs or time periods. The problem described is not about inconsistent outputs for the same input but about systematically poor performance for specific population groups.
Why C is Wrong: Transparency refers to openness about how the AI model operates, what data it uses, and how it makes decisions. The compliance officer has already assessed the system, suggesting sufficient transparency exists to identify the performance gap.
Why D is Wrong: Inclusiveness is a design principle ensuring AI systems are designed to serve all users regardless of background. While related to the outcome, the specific risk type described - differential predictive accuracy across population segments - is most precisely categorized as bias.
NEW QUESTION # 56
For which of the following situations would a human-in-the-loop be most recommended?
Answer: D
NEW QUESTION # 57
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: A
Explanation:
Basic Concept: Balancing automation with human oversight in vulnerability management requires understanding where AI adds efficiency and where human judgment is irreplaceable. CompTIA SecAI+ Study Guide emphasizes human-in-the-loop principles for high-stakes security decisions, particularly code changes in production systems.
Why A is Correct: Having AI handle triage and ticket creation leverages its ability to rapidly process and categorize large volumes of vulnerability findings, while requiring a software engineer to review and merge code changes maintains essential human oversight for production deployments. This balance maximizes automation benefits (faster triage at scale) while ensuring that actual code modifications to a million-line codebase receive appropriate human review before deployment.
Why B is Wrong: Having humans triage but AI merge code reverses the appropriate division. Manual triage of millions of lines worth of vulnerabilities is where the bottleneck exists. Allowing AI to autonomously merge code changes without human code review oversight creates unacceptable risk of introducing defects or vulnerabilities.
Why C is Wrong: Full manual triage and manual merging eliminates AI automation entirely, failing to address the speed requirement for reducing mean time to fix in a large codebase.
Why D is Wrong: Full AI automation including merging code changes removes essential human oversight from production code deployment. In a million-line codebase, autonomous AI code merging without human review could introduce critical errors or security vulnerabilities.
NEW QUESTION # 58
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