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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Basic AI Concepts Related to Cybersecurity | 17% | - AI Threat Landscape
|
| Topic 2: Securing AI Systems | 40% | - AI System Protection
|
| Topic 3: AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| Topic 4: AI-Assisted Security | 24% | - Operational Use of AI
|
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NEW QUESTION # 119
Which of the following job roles in an organizational governance structure develops a model from business use cases?
Answer: A
Explanation:
Basic Concept: In AI governance, each role holds distinct responsibilities. Understanding these roles is core to CompTIA SecAI+ Domain 4 (AI Governance, Risk, and Compliance).
Why D is Correct: The Data Scientist is responsible for translating business use cases into working AI/ML models. They analyze business requirements, identify the appropriate machine learning approach, and develop models that fulfill specific business objectives. According to the CompTIA SecAI+ Study Guide, data scientists bridge raw data and actionable AI solutions by building and validating models derived from business-driven needs.
Why A is Wrong: A Platform Architect designs and manages the infrastructure and technical platforms hosting AI systems. Their focus is architectural design of the environment, not model development from business use cases.
Why B is Wrong: An AI Risk Analyst identifies, evaluates, and mitigates risks associated with AI adoption.
Their role is governance and risk-oriented, not model creation.
Why C is Wrong: An MLOps Engineer operationalizes, deploys, monitors, and maintains AI models in production. They take models already built by data scientists and ensure reliable operation at scale, not develop them from business use cases.
NEW QUESTION # 120
A critical AI system cannot be shut down and must remain secure. Which of the following actions should be performed to apply controls?
Answer: A
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 # 121
Which of the following is the most concerning risk for a company that allows corporate end users to use public-facing large language models (LLMs)?
Answer: C
Explanation:
Basic Concept: When employees interact with public-facing LLMs, any data they input may be processed, logged, or used for model training by the third-party provider. This creates serious regulatory and compliance risks, particularly when sensitive corporate or customer data is involved. CompTIA SecAI+ flags data governance and regulatory compliance as primary concerns in enterprise AI adoption.
Why C is Correct: Submitting sensitive business data, PII, financial records, or proprietary information to public LLMs may violate data protection regulations such as GDPR, HIPAA, or CCPA. These violations can result in substantial fines, legal liability, and significant reputational damage. This represents the most severe and impactful organizational risk from corporate use of public LLMs.
Why A is Wrong: Hallucinations where LLMs generate plausible but incorrect information are a reliability concern. However, they do not expose the company to the level of legal and financial penalties that data regulatory violations create.
Why B is Wrong: Out-of-date acceptable use policies represent an internal governance gap. This is a policy management issue rather than a direct risk with immediate legal or financial consequences.
Why D is Wrong: While LLMs can potentially generate malicious code if deliberately prompted, this requires adversarial intent. For typical corporate users, accidental data disclosure to a public LLM represents a far more common and immediate organizational risk.
NEW QUESTION # 122
Users report that the output of a generative AI application seems unrelated to the prompts and contains offensive content. A security team investigates and determines that there was an on- path attack. Which of the following is the most likely attack method?
Answer: A
Explanation:
In an on-path attack, an adversary intercepts and manipulates traffic between the user and the AI system. Session hijacking allows the attacker to inject or alter prompts and responses, leading to unrelated or offensive output.
NEW QUESTION # 123
Which of the following attacks is most enabled by AI-generated content?
Answer: C
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 # 124
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