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| Section | Weight | Objectives |
|---|---|---|
| Securing AI Systems | 40% | - AI System Protection
|
| AI-Assisted Security | 24% | - Operational Use of AI
|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI and Machine Learning Fundamentals
|
| AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
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NEW QUESTION # 28
A loan company compares a machine learning model that was found on the dark web with the company ' s proprietary model. Using proprietary information, the company generates the following:
Input
Dark web model
Company ' s proprietary model
Jane A.
Approve
Approve
John B.
Deny
Deny
Ann J.
Deny
Deny
Joe H.
Approve
Approve
Which of the following should the AI red team recommend to mitigate risks for future deployments?
Answer: B
Explanation:
The matching behavior between the unauthorized dark-web model and the company ' s proprietary model strongly indicates a model-theft or unauthorized model-copying risk. Model encryption is the most appropriate control among the available choices because it directly protects proprietary model artifacts, including model weights and other sensitive intellectual property, from unauthorized access or extraction when stored or transferred. CompTIA SecAI+ specifically identifies model theft as an AI-system attack and lists encryption among the compensating controls used to protect AI systems. Parameter anonymization is not the primary protection for proprietary model weights and does not prevent unauthorized possession of the model itself. A prompt firewall analyzes prompts for malicious or prohibited input but does not protect model files from theft. Input validation similarly protects an application from malformed or malicious inputs rather than safeguarding the model ' s intellectual property. Therefore, strengthening protection of the model artifact through encryption is the most directly applicable mitigation for future deployments.
NEW QUESTION # 29
A SOC team has an AI agent that performs web searches and calls to the SOAR solution. The team is concerned about enterprise uptime and case resolution time.
Which of the following is the most appropriate use of the AI agent?
Answer: B
Explanation:
Basic Concept: AI agents in SOC environments can automate repetitive, rules-based response actions that previously required human intervention. When the primary concerns are enterprise uptime and case resolution time, the AI agent ' s ability to autonomously execute containment actions through SOAR is the most impactful application. CompTIA SecAI+ Study Guide covers AI agent use cases in security operations.
Why A is Correct: Using the AI agent to analyze incidents and execute containment actions through SOAR playbooks directly addresses both uptime and resolution time concerns. The agent can immediately analyze alert details, determine the appropriate playbook, and execute containment actions such as isolating compromised hosts or disabling compromised accounts autonomously, without waiting for human intervention. This dramatically reduces mean time to contain threats, improving both uptime and resolution speed.
Why B is Wrong: Enriching alerts with open-source intelligence improves analyst context but is a preparatory step rather than a response action. While valuable, it does not directly reduce resolution time by taking containment actions to stop ongoing threats.
Why C is Wrong: Aggregating metrics and generating leadership reports is an administrative function that consumes agent capacity for non-operational purposes. It improves visibility but does not directly improve uptime or case resolution time for active incidents.
Why D is Wrong: Creating tabletop exercises improves team preparedness over time through training scenarios. While beneficial for long-term capability development, it does not directly address the immediate concerns of enterprise uptime and active case resolution time.
NEW QUESTION # 30
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)?
Answer: C
Explanation:
MITRE ATLAS is specifically designed to capture adversarial tactics, techniques, and procedures (TTPs) targeting machine learning systems. It helps organizations assess both the probability and impact of AI/ML-related attacks, making it the most relevant threat-modeling resource.
NEW QUESTION # 31
Which of the following is the primary security risk when deploying AI models in production?
Answer: C
Explanation:
Basic Concept: When AI models are deployed in production, they interact with real data including sensitive business information, personal data, and confidential records. The intersection of AI capabilities and sensitive data creates significant security risks. CompTIA SecAI+ Exam Objectives identify data exposure as the primary production security risk for AI deployments.
Why D is Correct: Data exposure is the primary security risk in production AI deployments. AI models in production process sensitive data through queries and responses, and vulnerabilities such as prompt injection, model inversion attacks, insecure output handling, and misconfigured access controls can expose confidential training data, user PII, proprietary information, or system credentials. The consequences include regulatory violations, legal liability, and reputational damage, making data exposure the most critical ongoing security concern.
Why A is Wrong: GPU acceleration is a performance optimization technique that uses graphics processors for faster AI computation. While hardware security is important, GPU acceleration itself is not a security risk - it is a performance feature that does not inherently expose data.
Why B is Wrong: Model overfitting is a model quality issue where a model performs poorly on new data after memorizing training data too specifically. While it can indirectly contribute to data memorization, it is primarily a performance and generalization concern during development rather than a primary production security risk.
Why C is Wrong: Model encryption is a security control used to protect AI model weights from unauthorized access, not a risk itself. Framing a protection mechanism as a primary risk conflates controls with threats.
NEW QUESTION # 32
An AI-based human resources system screens resumes to select the most suitable candidate for an interview.
The system recommends mostly male candidates. Which of the following responsible AI concepts does this system violate?
Answer: A
Explanation:
Fairness is the responsible AI principle most clearly violated because the system is producing a disproportionately gender-skewed outcome in a consequential employment decision. CompTIA SecAI+ explicitly identifies fairness as a responsible AI consideration and identifies the introduction of bias as an AI risk. A resume-screening system should evaluate candidates using relevant employment criteria without systematically disadvantaging a protected or unrelated demographic group. Recommending mostly male candidates signals a potential bias in the training data, feature selection, historical examples, model design, or decision threshold and therefore requires fairness assessment and mitigation. Transparency concerns whether stakeholders have appropriate visibility into how the AI system operates. Explainability concerns whether individual decisions can be described in understandable terms. Reliability concerns whether the system performs consistently and safely according to its intended specifications. Those properties remain important, but none directly describes the demographic disparity presented in the scenario. Because the evidence specifically indicates unequal treatment or outcomes between demographic groups, fairness is the responsible AI principle most directly implicated.
NEW QUESTION # 33
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